id	sid	tid	token	lemma	pos
ajst-20808	1	1	academic	academic	ADJ
ajst-20808	1	2	journal	journal	NOUN
ajst-20808	1	3	of	of	ADP
ajst-20808	1	4	science	science	NOUN
ajst-20808	1	5	and	and	CCONJ
ajst-20808	1	6	technology	technology	NOUN
ajst-20808	1	7	issn	issn	NOUN
ajst-20808	1	8	:	:	PUNCT
ajst-20808	1	9	2771	2771	NUM
ajst-20808	1	10	-	-	SYM
ajst-20808	1	11	3032	3032	NUM
ajst-20808	1	12	|	|	NOUN
ajst-20808	1	13	vol	vol	NOUN
ajst-20808	1	14	.	.	PROPN
ajst-20808	2	1	10	10	NUM
ajst-20808	2	2	,	,	PUNCT
ajst-20808	2	3	no	no	INTJ
ajst-20808	2	4	.	.	NOUN
ajst-20808	2	5	3	3	NUM
ajst-20808	2	6	,	,	PUNCT
ajst-20808	2	7	2024	2024	NUM
ajst-20808	2	8	167	167	NUM
ajst-20808	2	9	a	a	DET
ajst-20808	2	10	comprehensive	comprehensive	ADJ
ajst-20808	2	11	evaluation	evaluation	NOUN
ajst-20808	2	12	and	and	CCONJ
ajst-20808	2	13	comparison	comparison	NOUN
ajst-20808	2	14	of	of	ADP
ajst-20808	2	15	enhanced	enhanced	ADJ
ajst-20808	2	16	learning	learning	NOUN
ajst-20808	2	17	methods	method	NOUN
ajst-20808	2	18	jintong	jintong	ADP
ajst-20808	2	19	song1	song1	PROPN
ajst-20808	2	20	,	,	PUNCT
ajst-20808	2	21	*	*	PROPN
ajst-20808	2	22	,	,	PUNCT
ajst-20808	2	23	houze	houze	PROPN
ajst-20808	2	24	liu2	liu2	PROPN
ajst-20808	2	25	,	,	PUNCT
ajst-20808	3	1	keqin	keqin	PROPN
ajst-20808	3	2	li3	li3	PROPN
ajst-20808	3	3	,	,	PUNCT
ajst-20808	3	4	jingxiao	jingxiao	PROPN
ajst-20808	3	5	tian4	tian4	PROPN
ajst-20808	3	6	,	,	PUNCT
ajst-20808	3	7	yuhong	yuhong	PROPN
ajst-20808	3	8	mo5	mo5	PROPN
ajst-20808	3	9	1boston	1boston	PROPN
ajst-20808	3	10	university	university	NOUN
ajst-20808	3	11	,	,	PUNCT
ajst-20808	3	12	boston	boston	PROPN
ajst-20808	3	13	ma	ma	PROPN
ajst-20808	3	14	usa	usa	PROPN
ajst-20808	3	15	2new	2new	PROPN
ajst-20808	3	16	york	york	PROPN
ajst-20808	3	17	university	university	PROPN
ajst-20808	3	18	,	,	PUNCT
ajst-20808	3	19	new	new	PROPN
ajst-20808	3	20	york	york	PROPN
ajst-20808	3	21	ny	ny	PROPN
ajst-20808	3	22	usa	usa	PROPN
ajst-20808	3	23	3ama	3ama	PROPN
ajst-20808	3	24	university	university	NOUN
ajst-20808	3	25	,	,	PUNCT
ajst-20808	3	26	philippines	philippine	NOUN
ajst-20808	3	27	4san	4san	PROPN
ajst-20808	3	28	diego	diego	PROPN
ajst-20808	3	29	state	state	PROPN
ajst-20808	3	30	university	university	PROPN
ajst-20808	3	31	,	,	PUNCT
ajst-20808	3	32	san	san	PROPN
ajst-20808	3	33	diego	diego	PROPN
ajst-20808	3	34	ca	ca	PROPN
ajst-20808	3	35	usa	usa	PROPN
ajst-20808	3	36	5carnegie	5carnegie	PROPN
ajst-20808	3	37	mellon	mellon	PROPN
ajst-20808	3	38	university	university	PROPN
ajst-20808	3	39	,	,	PUNCT
ajst-20808	3	40	pittsburgh	pittsburgh	PROPN
ajst-20808	3	41	pa	pa	PROPN
ajst-20808	3	42	usa	usa	PROPN
ajst-20808	3	43	*	*	PUNCT
ajst-20808	3	44	corresponding	correspond	VERB
ajst-20808	3	45	author	author	NOUN
ajst-20808	3	46	:	:	PUNCT
ajst-20808	3	47	jintong	jintong	ADP
ajst-20808	3	48	song	song	NOUN
ajst-20808	3	49	(	(	PUNCT
ajst-20808	3	50	email	email	NOUN
ajst-20808	3	51	:	:	PUNCT
ajst-20808	3	52	jintongs@bu.edu	jintongs@bu.edu	PROPN
ajst-20808	3	53	)	)	PUNCT
ajst-20808	3	54	abstract	abstract	NOUN
ajst-20808	3	55	:	:	PUNCT
ajst-20808	3	56	this	this	DET
ajst-20808	3	57	paper	paper	NOUN
ajst-20808	3	58	provides	provide	VERB
ajst-20808	3	59	a	a	DET
ajst-20808	3	60	comprehensive	comprehensive	ADJ
ajst-20808	3	61	evaluation	evaluation	NOUN
ajst-20808	3	62	and	and	CCONJ
ajst-20808	3	63	comparison	comparison	NOUN
ajst-20808	3	64	of	of	ADP
ajst-20808	3	65	current	current	ADJ
ajst-20808	3	66	reinforcement	reinforcement	NOUN
ajst-20808	3	67	learning	learning	NOUN
ajst-20808	3	68	methods	method	NOUN
ajst-20808	3	69	.	.	PUNCT
ajst-20808	4	1	by	by	ADP
ajst-20808	4	2	analyzing	analyze	VERB
ajst-20808	4	3	the	the	DET
ajst-20808	4	4	strengths	strength	NOUN
ajst-20808	4	5	and	and	CCONJ
ajst-20808	4	6	weaknesses	weakness	NOUN
ajst-20808	4	7	of	of	ADP
ajst-20808	4	8	the	the	DET
ajst-20808	4	9	main	main	ADJ
ajst-20808	4	10	methods	method	NOUN
ajst-20808	4	11	,	,	PUNCT
ajst-20808	4	12	such	such	ADJ
ajst-20808	4	13	as	as	ADP
ajst-20808	4	14	value	value	NOUN
ajst-20808	4	15	function	function	NOUN
ajst-20808	4	16	-	-	PUNCT
ajst-20808	4	17	based	base	VERB
ajst-20808	4	18	,	,	PUNCT
ajst-20808	4	19	strategy	strategy	NOUN
ajst-20808	4	20	gradient	gradient	NOUN
ajst-20808	4	21	-	-	PUNCT
ajst-20808	4	22	based	base	VERB
ajst-20808	4	23	,	,	PUNCT
ajst-20808	4	24	and	and	CCONJ
ajst-20808	4	25	value	value	NOUN
ajst-20808	4	26	and	and	CCONJ
ajst-20808	4	27	strategy	strategy	NOUN
ajst-20808	4	28	-	-	PUNCT
ajst-20808	4	29	based	base	VERB
ajst-20808	4	30	methods	method	NOUN
ajst-20808	4	31	,	,	PUNCT
ajst-20808	4	32	the	the	DET
ajst-20808	4	33	differences	difference	NOUN
ajst-20808	4	34	in	in	ADP
ajst-20808	4	35	their	their	PRON
ajst-20808	4	36	performances	performance	NOUN
ajst-20808	4	37	on	on	ADP
ajst-20808	4	38	standard	standard	ADJ
ajst-20808	4	39	problems	problem	NOUN
ajst-20808	4	40	and	and	CCONJ
ajst-20808	4	41	the	the	DET
ajst-20808	4	42	applicable	applicable	ADJ
ajst-20808	4	43	scenarios	scenario	NOUN
ajst-20808	4	44	are	be	AUX
ajst-20808	4	45	explored	explore	VERB
ajst-20808	4	46	.	.	PUNCT
ajst-20808	5	1	meanwhile	meanwhile	ADV
ajst-20808	5	2	,	,	PUNCT
ajst-20808	5	3	other	other	ADJ
ajst-20808	5	4	methods	method	NOUN
ajst-20808	5	5	such	such	ADJ
ajst-20808	5	6	as	as	ADP
ajst-20808	5	7	monte	monte	PROPN
ajst-20808	5	8	carlo	carlo	PROPN
ajst-20808	5	9	tree	tree	NOUN
ajst-20808	5	10	search	search	NOUN
ajst-20808	5	11	(	(	PUNCT
ajst-20808	5	12	mcts	mct	NOUN
ajst-20808	5	13	)	)	PUNCT
ajst-20808	5	14	and	and	CCONJ
ajst-20808	5	15	evolutionary	evolutionary	ADJ
ajst-20808	5	16	methods	method	NOUN
ajst-20808	5	17	are	be	AUX
ajst-20808	5	18	also	also	ADV
ajst-20808	5	19	briefly	briefly	ADV
ajst-20808	5	20	introduced	introduce	VERB
ajst-20808	5	21	.	.	PUNCT
ajst-20808	6	1	through	through	ADP
ajst-20808	6	2	the	the	DET
ajst-20808	6	3	research	research	NOUN
ajst-20808	6	4	and	and	CCONJ
ajst-20808	6	5	analysis	analysis	NOUN
ajst-20808	6	6	in	in	ADP
ajst-20808	6	7	this	this	DET
ajst-20808	6	8	paper	paper	NOUN
ajst-20808	6	9	,	,	PUNCT
ajst-20808	6	10	it	it	PRON
ajst-20808	6	11	provides	provide	VERB
ajst-20808	6	12	reference	reference	NOUN
ajst-20808	6	13	and	and	CCONJ
ajst-20808	6	14	guidance	guidance	NOUN
ajst-20808	6	15	for	for	ADP
ajst-20808	6	16	choosing	choose	VERB
ajst-20808	6	17	appropriate	appropriate	ADJ
ajst-20808	6	18	reinforcement	reinforcement	NOUN
ajst-20808	6	19	learning	learning	NOUN
ajst-20808	6	20	methods	method	NOUN
ajst-20808	6	21	,	,	PUNCT
ajst-20808	6	22	and	and	CCONJ
ajst-20808	6	23	promotes	promote	VERB
ajst-20808	6	24	the	the	DET
ajst-20808	6	25	development	development	NOUN
ajst-20808	6	26	and	and	CCONJ
ajst-20808	6	27	application	application	NOUN
ajst-20808	6	28	of	of	ADP
ajst-20808	6	29	reinforcement	reinforcement	NOUN
ajst-20808	6	30	learning	learning	NOUN
ajst-20808	6	31	techniques	technique	NOUN
ajst-20808	6	32	in	in	ADP
ajst-20808	6	33	practical	practical	ADJ
ajst-20808	6	34	applications	application	NOUN
ajst-20808	6	35	.	.	PUNCT
ajst-20808	7	1	keywords	keyword	NOUN
ajst-20808	7	2	:	:	PUNCT
ajst-20808	7	3	reinforcement	reinforcement	NOUN
ajst-20808	7	4	learning	learning	NOUN
ajst-20808	7	5	,	,	PUNCT
ajst-20808	7	6	method	method	ADJ
ajst-20808	7	7	comparison	comparison	NOUN
ajst-20808	7	8	,	,	PUNCT
ajst-20808	7	9	advantages	advantage	NOUN
ajst-20808	7	10	and	and	CCONJ
ajst-20808	7	11	disadvantages	disadvantage	VERB
ajst-20808	7	12	analysis	analysis	NOUN
ajst-20808	7	13	.	.	PUNCT
ajst-20808	8	1	1	1	X
ajst-20808	8	2	.	.	X
ajst-20808	8	3	introduction	introduction	NOUN
ajst-20808	8	4	with	with	ADP
ajst-20808	8	5	the	the	DET
ajst-20808	8	6	continuous	continuous	ADJ
ajst-20808	8	7	development	development	NOUN
ajst-20808	8	8	and	and	CCONJ
ajst-20808	8	9	depth	depth	NOUN
ajst-20808	8	10	of	of	ADP
ajst-20808	8	11	the	the	DET
ajst-20808	8	12	field	field	NOUN
ajst-20808	8	13	of	of	ADP
ajst-20808	8	14	artificial	artificial	ADJ
ajst-20808	8	15	intelligence	intelligence	NOUN
ajst-20808	8	16	,	,	PUNCT
ajst-20808	8	17	reinforcement	reinforcement	NOUN
ajst-20808	8	18	learning	learning	NOUN
ajst-20808	8	19	,	,	PUNCT
ajst-20808	8	20	as	as	ADP
ajst-20808	8	21	an	an	DET
ajst-20808	8	22	important	important	ADJ
ajst-20808	8	23	learning	learning	NOUN
ajst-20808	8	24	paradigm	paradigm	NOUN
ajst-20808	8	25	,	,	PUNCT
ajst-20808	8	26	is	be	AUX
ajst-20808	8	27	receiving	receive	VERB
ajst-20808	8	28	more	more	ADJ
ajst-20808	8	29	and	and	CCONJ
ajst-20808	8	30	more	more	ADJ
ajst-20808	8	31	attention	attention	NOUN
ajst-20808	8	32	and	and	CCONJ
ajst-20808	8	33	application	application	NOUN
ajst-20808	8	34	,	,	PUNCT
ajst-20808	8	35	which	which	PRON
ajst-20808	8	36	were	be	AUX
ajst-20808	8	37	usually	usually	ADV
ajst-20808	8	38	solved	solve	VERB
ajst-20808	8	39	by	by	ADP
ajst-20808	8	40	model	model	NOUN
ajst-20808	8	41	-	-	PUNCT
ajst-20808	8	42	based	base	VERB
ajst-20808	8	43	solutions	solution	NOUN
ajst-20808	8	44	,	,	PUNCT
ajst-20808	8	45	have	have	VERB
ajst-20808	8	46	an	an	DET
ajst-20808	8	47	opportunity	opportunity	NOUN
ajst-20808	8	48	to	to	PART
ajst-20808	8	49	be	be	AUX
ajst-20808	8	50	solved	solve	VERB
ajst-20808	8	51	with	with	ADP
ajst-20808	8	52	data	datum	NOUN
ajst-20808	8	53	driven	drive	VERB
ajst-20808	8	54	solutions.[1	solutions.[1	PUNCT
ajst-20808	8	55	]	]	PUNCT
ajst-20808	8	56	reinforcement	reinforcement	NOUN
ajst-20808	8	57	learning	learning	NOUN
ajst-20808	8	58	learns	learn	VERB
ajst-20808	8	59	through	through	ADP
ajst-20808	8	60	the	the	DET
ajst-20808	8	61	interaction	interaction	NOUN
ajst-20808	8	62	between	between	ADP
ajst-20808	8	63	the	the	DET
ajst-20808	8	64	intelligent	intelligent	ADJ
ajst-20808	8	65	body	body	NOUN
ajst-20808	8	66	and	and	CCONJ
ajst-20808	8	67	the	the	DET
ajst-20808	8	68	environment	environment	NOUN
ajst-20808	8	69	,	,	PUNCT
ajst-20808	8	70	enabling	enable	VERB
ajst-20808	8	71	the	the	DET
ajst-20808	8	72	intelligent	intelligent	ADJ
ajst-20808	8	73	body	body	NOUN
ajst-20808	8	74	to	to	PART
ajst-20808	8	75	maximize	maximize	VERB
ajst-20808	8	76	the	the	DET
ajst-20808	8	77	cumulative	cumulative	ADJ
ajst-20808	8	78	rewards	reward	NOUN
ajst-20808	8	79	through	through	ADP
ajst-20808	8	80	trial	trial	NOUN
ajst-20808	8	81	-	-	PUNCT
ajst-20808	8	82	and	and	CCONJ
ajst-20808	8	83	-	-	PUNCT
ajst-20808	8	84	error	error	NOUN
ajst-20808	8	85	learning	learning	NOUN
ajst-20808	8	86	in	in	ADP
ajst-20808	8	87	unknown	unknown	ADJ
ajst-20808	8	88	environments	environment	NOUN
ajst-20808	8	89	,	,	PUNCT
ajst-20808	8	90	thus	thus	ADV
ajst-20808	8	91	solving	solve	VERB
ajst-20808	8	92	many	many	ADJ
ajst-20808	8	93	problems	problem	NOUN
ajst-20808	8	94	that	that	PRON
ajst-20808	8	95	can	can	AUX
ajst-20808	8	96	not	not	PART
ajst-20808	8	97	be	be	AUX
ajst-20808	8	98	solved	solve	VERB
ajst-20808	8	99	by	by	ADP
ajst-20808	8	100	traditional	traditional	ADJ
ajst-20808	8	101	machine	machine	NOUN
ajst-20808	8	102	learning	learning	NOUN
ajst-20808	8	103	methods	method	NOUN
ajst-20808	8	104	,	,	PUNCT
ajst-20808	8	105	such	such	ADJ
ajst-20808	8	106	as	as	ADP
ajst-20808	8	107	decision	decision	NOUN
ajst-20808	8	108	making	making	NOUN
ajst-20808	8	109	and	and	CCONJ
ajst-20808	8	110	control	control	VERB
ajst-20808	8	111	strategy	strategy	NOUN
ajst-20808	8	112	optimization	optimization	NOUN
ajst-20808	8	113	.	.	PUNCT
ajst-20808	9	1	however	however	ADV
ajst-20808	9	2	,	,	PUNCT
ajst-20808	9	3	with	with	ADP
ajst-20808	9	4	the	the	DET
ajst-20808	9	5	increasing	increase	VERB
ajst-20808	9	6	complexity	complexity	NOUN
ajst-20808	9	7	of	of	ADP
ajst-20808	9	8	problems	problem	NOUN
ajst-20808	9	9	,	,	PUNCT
ajst-20808	9	10	the	the	DET
ajst-20808	9	11	advantages	advantage	NOUN
ajst-20808	9	12	and	and	CCONJ
ajst-20808	9	13	limitations	limitation	NOUN
ajst-20808	9	14	of	of	ADP
ajst-20808	9	15	various	various	ADJ
ajst-20808	9	16	reinforcement	reinforcement	NOUN
ajst-20808	9	17	learning	learning	NOUN
ajst-20808	9	18	methods	method	NOUN
ajst-20808	9	19	have	have	AUX
ajst-20808	9	20	gradually	gradually	ADV
ajst-20808	9	21	emerged	emerge	VERB
ajst-20808	9	22	.	.	PUNCT
ajst-20808	10	1	the	the	DET
ajst-20808	10	2	purpose	purpose	NOUN
ajst-20808	10	3	of	of	ADP
ajst-20808	10	4	this	this	DET
ajst-20808	10	5	paper	paper	NOUN
ajst-20808	10	6	is	be	AUX
ajst-20808	10	7	to	to	PART
ajst-20808	10	8	conduct	conduct	VERB
ajst-20808	10	9	a	a	DET
ajst-20808	10	10	comprehensive	comprehensive	ADJ
ajst-20808	10	11	evaluation	evaluation	NOUN
ajst-20808	10	12	and	and	CCONJ
ajst-20808	10	13	comparison	comparison	NOUN
ajst-20808	10	14	of	of	ADP
ajst-20808	10	15	current	current	ADJ
ajst-20808	10	16	reinforcement	reinforcement	NOUN
ajst-20808	10	17	learning	learning	NOUN
ajst-20808	10	18	methods	method	NOUN
ajst-20808	10	19	,	,	PUNCT
ajst-20808	10	20	to	to	PART
ajst-20808	10	21	explore	explore	VERB
ajst-20808	10	22	the	the	DET
ajst-20808	10	23	performance	performance	NOUN
ajst-20808	10	24	differences	difference	NOUN
ajst-20808	10	25	of	of	ADP
ajst-20808	10	26	different	different	ADJ
ajst-20808	10	27	methods	method	NOUN
ajst-20808	10	28	on	on	ADP
ajst-20808	10	29	standard	standard	ADJ
ajst-20808	10	30	problems	problem	NOUN
ajst-20808	10	31	,	,	PUNCT
ajst-20808	10	32	and	and	CCONJ
ajst-20808	10	33	to	to	PART
ajst-20808	10	34	analyze	analyze	VERB
ajst-20808	10	35	their	their	PRON
ajst-20808	10	36	applicable	applicable	ADJ
ajst-20808	10	37	scenarios	scenario	NOUN
ajst-20808	10	38	and	and	CCONJ
ajst-20808	10	39	advantages	advantage	NOUN
ajst-20808	10	40	and	and	CCONJ
ajst-20808	10	41	disadvantages	disadvantage	NOUN
ajst-20808	10	42	.	.	PUNCT
ajst-20808	11	1	an	an	DET
ajst-20808	11	2	in	in	ADP
ajst-20808	11	3	-	-	PUNCT
ajst-20808	11	4	depth	depth	NOUN
ajst-20808	11	5	discussion	discussion	NOUN
ajst-20808	11	6	will	will	AUX
ajst-20808	11	7	be	be	AUX
ajst-20808	11	8	conducted	conduct	VERB
ajst-20808	11	9	on	on	ADP
ajst-20808	11	10	the	the	DET
ajst-20808	11	11	main	main	ADJ
ajst-20808	11	12	methods	method	NOUN
ajst-20808	11	13	,	,	PUNCT
ajst-20808	11	14	such	such	ADJ
ajst-20808	11	15	as	as	ADP
ajst-20808	11	16	value	value	NOUN
ajst-20808	11	17	function	function	NOUN
ajst-20808	11	18	-	-	PUNCT
ajst-20808	11	19	based	base	VERB
ajst-20808	11	20	methods	method	NOUN
ajst-20808	11	21	,	,	PUNCT
ajst-20808	11	22	strategy	strategy	NOUN
ajst-20808	11	23	gradientbased	gradientbase	VERB
ajst-20808	11	24	methods	method	NOUN
ajst-20808	11	25	,	,	PUNCT
ajst-20808	11	26	and	and	CCONJ
ajst-20808	11	27	value	value	NOUN
ajst-20808	11	28	and	and	CCONJ
ajst-20808	11	29	strategy	strategy	NOUN
ajst-20808	11	30	-	-	PUNCT
ajst-20808	11	31	based	base	VERB
ajst-20808	11	32	methods	method	NOUN
ajst-20808	11	33	,	,	PUNCT
ajst-20808	11	34	and	and	CCONJ
ajst-20808	11	35	their	their	PRON
ajst-20808	11	36	application	application	NOUN
ajst-20808	11	37	potentials	potential	VERB
ajst-20808	11	38	in	in	ADP
ajst-20808	11	39	solving	solve	VERB
ajst-20808	11	40	real	real	ADJ
ajst-20808	11	41	-	-	PUNCT
ajst-20808	11	42	world	world	NOUN
ajst-20808	11	43	problems	problem	NOUN
ajst-20808	11	44	will	will	AUX
ajst-20808	11	45	be	be	AUX
ajst-20808	11	46	assessed	assess	VERB
ajst-20808	11	47	.	.	PUNCT
ajst-20808	12	1	in	in	ADP
ajst-20808	12	2	addition	addition	NOUN
ajst-20808	12	3	,	,	PUNCT
ajst-20808	12	4	some	some	DET
ajst-20808	12	5	other	other	ADJ
ajst-20808	12	6	methods	method	NOUN
ajst-20808	12	7	,	,	PUNCT
ajst-20808	12	8	such	such	ADJ
ajst-20808	12	9	as	as	ADP
ajst-20808	12	10	monte	monte	PROPN
ajst-20808	12	11	carlo	carlo	PROPN
ajst-20808	12	12	tree	tree	NOUN
ajst-20808	12	13	search	search	NOUN
ajst-20808	12	14	(	(	PUNCT
ajst-20808	12	15	mcts	mct	NOUN
ajst-20808	12	16	)	)	PUNCT
ajst-20808	12	17	,	,	PUNCT
ajst-20808	12	18	evolutionary	evolutionary	ADJ
ajst-20808	12	19	methods	method	NOUN
ajst-20808	12	20	,	,	PUNCT
ajst-20808	12	21	etc	etc	X
ajst-20808	12	22	.	.	X
ajst-20808	12	23	,	,	PUNCT
ajst-20808	12	24	will	will	AUX
ajst-20808	12	25	be	be	AUX
ajst-20808	12	26	briefly	briefly	ADV
ajst-20808	12	27	introduced	introduce	VERB
ajst-20808	12	28	and	and	CCONJ
ajst-20808	12	29	their	their	PRON
ajst-20808	12	30	strengths	strength	NOUN
ajst-20808	12	31	and	and	CCONJ
ajst-20808	12	32	limitations	limitation	NOUN
ajst-20808	12	33	in	in	ADP
ajst-20808	12	34	specific	specific	ADJ
ajst-20808	12	35	problem	problem	NOUN
ajst-20808	12	36	domains	domain	NOUN
ajst-20808	12	37	will	will	AUX
ajst-20808	12	38	be	be	AUX
ajst-20808	12	39	explored	explore	VERB
ajst-20808	12	40	.	.	PUNCT
ajst-20808	13	1	through	through	ADP
ajst-20808	13	2	the	the	DET
ajst-20808	13	3	research	research	NOUN
ajst-20808	13	4	and	and	CCONJ
ajst-20808	13	5	analysis	analysis	NOUN
ajst-20808	13	6	in	in	ADP
ajst-20808	13	7	this	this	DET
ajst-20808	13	8	paper	paper	NOUN
ajst-20808	13	9	,	,	PUNCT
ajst-20808	13	10	it	it	PRON
ajst-20808	13	11	is	be	AUX
ajst-20808	13	12	hoped	hope	VERB
ajst-20808	13	13	that	that	SCONJ
ajst-20808	13	14	it	it	PRON
ajst-20808	13	15	can	can	AUX
ajst-20808	13	16	provide	provide	VERB
ajst-20808	13	17	reference	reference	NOUN
ajst-20808	13	18	and	and	CCONJ
ajst-20808	13	19	guidance	guidance	NOUN
ajst-20808	13	20	for	for	ADP
ajst-20808	13	21	the	the	DET
ajst-20808	13	22	selection	selection	NOUN
ajst-20808	13	23	of	of	ADP
ajst-20808	13	24	appropriate	appropriate	ADJ
ajst-20808	13	25	reinforcement	reinforcement	NOUN
ajst-20808	13	26	learning	learning	NOUN
ajst-20808	13	27	methods	method	NOUN
ajst-20808	13	28	and	and	CCONJ
ajst-20808	13	29	further	far	ADV
ajst-20808	13	30	promote	promote	VERB
ajst-20808	13	31	the	the	DET
ajst-20808	13	32	development	development	NOUN
ajst-20808	13	33	and	and	CCONJ
ajst-20808	13	34	application	application	NOUN
ajst-20808	13	35	of	of	ADP
ajst-20808	13	36	reinforcement	reinforcement	NOUN
ajst-20808	13	37	learning	learning	NOUN
ajst-20808	13	38	techniques	technique	NOUN
ajst-20808	13	39	in	in	ADP
ajst-20808	13	40	practical	practical	ADJ
ajst-20808	13	41	applications	application	NOUN
ajst-20808	13	42	.	.	PUNCT
ajst-20808	14	1	2	2	X
ajst-20808	14	2	.	.	X
ajst-20808	14	3	theoretical	theoretical	ADJ
ajst-20808	14	4	foundation	foundation	NOUN
ajst-20808	14	5	of	of	ADP
ajst-20808	14	6	reinforcement	reinforcement	NOUN
ajst-20808	14	7	learning	learn	VERB
ajst-20808	14	8	2.1	2.1	NUM
ajst-20808	14	9	.	.	PUNCT
ajst-20808	15	1	reinforcement	reinforcement	NOUN
ajst-20808	15	2	learning	learning	NOUN
ajst-20808	15	3	definition	definition	NOUN
ajst-20808	15	4	and	and	CCONJ
ajst-20808	15	5	principle	principle	NOUN
ajst-20808	15	6	reinforcement	reinforcement	NOUN
ajst-20808	15	7	learning	learning	NOUN
ajst-20808	15	8	is	be	AUX
ajst-20808	15	9	a	a	DET
ajst-20808	15	10	machine	machine	NOUN
ajst-20808	15	11	learning	learning	NOUN
ajst-20808	15	12	paradigm	paradigm	NOUN
ajst-20808	15	13	designed	design	VERB
ajst-20808	15	14	to	to	PART
ajst-20808	15	15	enable	enable	VERB
ajst-20808	15	16	an	an	DET
ajst-20808	15	17	intelligent	intelligent	ADJ
ajst-20808	15	18	body	body	NOUN
ajst-20808	15	19	to	to	PART
ajst-20808	15	20	learn	learn	VERB
ajst-20808	15	21	optimal	optimal	ADJ
ajst-20808	15	22	behavioral	behavioral	ADJ
ajst-20808	15	23	strategies	strategy	NOUN
ajst-20808	15	24	through	through	ADP
ajst-20808	15	25	interaction	interaction	NOUN
ajst-20808	15	26	with	with	ADP
ajst-20808	15	27	the	the	DET
ajst-20808	15	28	environment	environment	NOUN
ajst-20808	15	29	in	in	ADP
ajst-20808	15	30	order	order	NOUN
ajst-20808	15	31	to	to	PART
ajst-20808	15	32	maximize	maximize	VERB
ajst-20808	15	33	cumulative	cumulative	ADJ
ajst-20808	15	34	rewards	reward	NOUN
ajst-20808	15	35	.	.	PUNCT
ajst-20808	16	1	in	in	ADP
ajst-20808	16	2	reinforcement	reinforcement	NOUN
ajst-20808	16	3	learning	learning	NOUN
ajst-20808	16	4	,	,	PUNCT
ajst-20808	16	5	an	an	DET
ajst-20808	16	6	intelligent	intelligent	ADJ
ajst-20808	16	7	body	body	NOUN
ajst-20808	16	8	learns	learn	VERB
ajst-20808	16	9	by	by	ADP
ajst-20808	16	10	observing	observe	VERB
ajst-20808	16	11	the	the	DET
ajst-20808	16	12	state	state	NOUN
ajst-20808	16	13	of	of	ADP
ajst-20808	16	14	the	the	DET
ajst-20808	16	15	environment	environment	NOUN
ajst-20808	16	16	and	and	CCONJ
ajst-20808	16	17	performing	perform	VERB
ajst-20808	16	18	actions	action	NOUN
ajst-20808	16	19	,	,	PUNCT
ajst-20808	16	20	and	and	CCONJ
ajst-20808	16	21	receives	receive	VERB
ajst-20808	16	22	rewards	reward	NOUN
ajst-20808	16	23	or	or	CCONJ
ajst-20808	16	24	punishments	punishment	NOUN
ajst-20808	16	25	from	from	ADP
ajst-20808	16	26	the	the	DET
ajst-20808	16	27	environment	environment	NOUN
ajst-20808	16	28	to	to	PART
ajst-20808	16	29	adjust	adjust	VERB
ajst-20808	16	30	its	its	PRON
ajst-20808	16	31	behavior	behavior	NOUN
ajst-20808	16	32	.	.	PUNCT
ajst-20808	17	1	the	the	DET
ajst-20808	17	2	core	core	ADJ
ajst-20808	17	3	principles	principle	NOUN
ajst-20808	17	4	of	of	ADP
ajst-20808	17	5	reinforcement	reinforcement	NOUN
ajst-20808	17	6	learning	learning	NOUN
ajst-20808	17	7	are	be	AUX
ajst-20808	17	8	built	build	VERB
ajst-20808	17	9	on	on	ADP
ajst-20808	17	10	the	the	DET
ajst-20808	17	11	framework	framework	NOUN
ajst-20808	17	12	of	of	ADP
ajst-20808	17	13	markov	markov	NOUN
ajst-20808	17	14	decision	decision	NOUN
ajst-20808	17	15	processes	process	NOUN
ajst-20808	17	16	(	(	PUNCT
ajst-20808	17	17	mdps).mdps	mdps).mdp	NOUN
ajst-20808	17	18	provide	provide	VERB
ajst-20808	17	19	formal	formal	ADJ
ajst-20808	17	20	mathematical	mathematical	ADJ
ajst-20808	17	21	models	model	NOUN
ajst-20808	17	22	describing	describe	VERB
ajst-20808	17	23	the	the	DET
ajst-20808	17	24	interaction	interaction	NOUN
ajst-20808	17	25	of	of	ADP
ajst-20808	17	26	an	an	DET
ajst-20808	17	27	intelligent	intelligent	ADJ
ajst-20808	17	28	with	with	ADP
ajst-20808	17	29	its	its	PRON
ajst-20808	17	30	environment	environment	NOUN
ajst-20808	17	31	.	.	PUNCT
ajst-20808	18	1	in	in	ADP
ajst-20808	18	2	mdp	mdp	PROPN
ajst-20808	18	3	,	,	PUNCT
ajst-20808	18	4	states	state	NOUN
ajst-20808	18	5	represent	represent	VERB
ajst-20808	18	6	specific	specific	ADJ
ajst-20808	18	7	situations	situation	NOUN
ajst-20808	18	8	or	or	CCONJ
ajst-20808	18	9	configurations	configuration	NOUN
ajst-20808	18	10	of	of	ADP
ajst-20808	18	11	the	the	DET
ajst-20808	18	12	environment	environment	NOUN
ajst-20808	18	13	,	,	PUNCT
ajst-20808	18	14	and	and	CCONJ
ajst-20808	18	15	intelligences	intelligence	NOUN
ajst-20808	18	16	take	take	VERB
ajst-20808	18	17	different	different	ADJ
ajst-20808	18	18	actions	action	NOUN
ajst-20808	18	19	in	in	ADP
ajst-20808	18	20	different	different	ADJ
ajst-20808	18	21	states	state	NOUN
ajst-20808	18	22	.	.	PUNCT
ajst-20808	19	1	states	state	NOUN
ajst-20808	19	2	can	can	AUX
ajst-20808	19	3	be	be	AUX
ajst-20808	19	4	discrete	discrete	ADJ
ajst-20808	19	5	or	or	CCONJ
ajst-20808	19	6	continuous	continuous	ADJ
ajst-20808	19	7	.	.	PUNCT
ajst-20808	20	1	actions	action	NOUN
ajst-20808	20	2	represent	represent	VERB
ajst-20808	20	3	operations	operation	NOUN
ajst-20808	20	4	or	or	CCONJ
ajst-20808	20	5	strategies	strategy	NOUN
ajst-20808	20	6	that	that	PRON
ajst-20808	20	7	an	an	DET
ajst-20808	20	8	intelligent	intelligent	ADJ
ajst-20808	20	9	body	body	NOUN
ajst-20808	20	10	can	can	AUX
ajst-20808	20	11	perform	perform	VERB
ajst-20808	20	12	.	.	PUNCT
ajst-20808	21	1	in	in	ADP
ajst-20808	21	2	each	each	DET
ajst-20808	21	3	state	state	NOUN
ajst-20808	21	4	,	,	PUNCT
ajst-20808	21	5	the	the	DET
ajst-20808	21	6	intelligent	intelligent	ADJ
ajst-20808	21	7	body	body	NOUN
ajst-20808	21	8	can	can	AUX
ajst-20808	21	9	choose	choose	VERB
ajst-20808	21	10	to	to	PART
ajst-20808	21	11	perform	perform	VERB
ajst-20808	21	12	different	different	ADJ
ajst-20808	21	13	actions	action	NOUN
ajst-20808	21	14	.	.	PUNCT
ajst-20808	22	1	rewards	reward	NOUN
ajst-20808	22	2	denote	denote	VERB
ajst-20808	22	3	the	the	DET
ajst-20808	22	4	feedback	feedback	NOUN
ajst-20808	22	5	that	that	PRON
ajst-20808	22	6	the	the	DET
ajst-20808	22	7	intelligent	intelligent	ADJ
ajst-20808	22	8	body	body	NOUN
ajst-20808	22	9	receives	receive	VERB
ajst-20808	22	10	from	from	ADP
ajst-20808	22	11	the	the	DET
ajst-20808	22	12	environment	environment	NOUN
ajst-20808	22	13	after	after	ADP
ajst-20808	22	14	performing	perform	VERB
ajst-20808	22	15	an	an	DET
ajst-20808	22	16	action	action	NOUN
ajst-20808	22	17	.	.	PUNCT
ajst-20808	23	1	rewards	reward	NOUN
ajst-20808	23	2	can	can	AUX
ajst-20808	23	3	be	be	AUX
ajst-20808	23	4	positive	positive	ADJ
ajst-20808	23	5	,	,	PUNCT
ajst-20808	23	6	negative	negative	ADJ
ajst-20808	23	7	,	,	PUNCT
ajst-20808	23	8	or	or	CCONJ
ajst-20808	23	9	zero	zero	NUM
ajst-20808	23	10	,	,	PUNCT
ajst-20808	23	11	and	and	CCONJ
ajst-20808	23	12	are	be	AUX
ajst-20808	23	13	used	use	VERB
ajst-20808	23	14	to	to	PART
ajst-20808	23	15	indicate	indicate	VERB
ajst-20808	23	16	the	the	DET
ajst-20808	23	17	degree	degree	NOUN
ajst-20808	23	18	of	of	ADP
ajst-20808	23	19	merit	merit	NOUN
ajst-20808	23	20	of	of	ADP
ajst-20808	23	21	the	the	DET
ajst-20808	23	22	action	action	NOUN
ajst-20808	23	23	.	.	PUNCT
ajst-20808	24	1	policies	policy	NOUN
ajst-20808	24	2	denote	denote	VERB
ajst-20808	24	3	the	the	DET
ajst-20808	24	4	strategy	strategy	NOUN
ajst-20808	24	5	by	by	ADP
ajst-20808	24	6	which	which	PRON
ajst-20808	24	7	an	an	DET
ajst-20808	24	8	intelligent	intelligent	ADJ
ajst-20808	24	9	body	body	NOUN
ajst-20808	24	10	chooses	choose	VERB
ajst-20808	24	11	an	an	DET
ajst-20808	24	12	action	action	NOUN
ajst-20808	24	13	in	in	ADP
ajst-20808	24	14	a	a	DET
ajst-20808	24	15	given	give	VERB
ajst-20808	24	16	state	state	NOUN
ajst-20808	24	17	,	,	PUNCT
ajst-20808	24	18	and	and	CCONJ
ajst-20808	24	19	the	the	DET
ajst-20808	24	20	strategy	strategy	NOUN
ajst-20808	24	21	can	can	AUX
ajst-20808	24	22	be	be	AUX
ajst-20808	24	23	deterministic	deterministic	ADJ
ajst-20808	24	24	or	or	CCONJ
ajst-20808	24	25	stochastic	stochastic	NOUN
ajst-20808	24	26	.	.	PUNCT
ajst-20808	25	1	combining	combine	VERB
ajst-20808	25	2	the	the	DET
ajst-20808	25	3	change	change	NOUN
ajst-20808	25	4	detection	detection	NOUN
ajst-20808	25	5	task	task	NOUN
ajst-20808	25	6	with	with	ADP
ajst-20808	25	7	reinforcement	reinforcement	NOUN
ajst-20808	25	8	learning	learning	NOUN
ajst-20808	25	9	utilizes	utilize	VERB
ajst-20808	25	10	the	the	DET
ajst-20808	25	11	framework	framework	NOUN
ajst-20808	25	12	of	of	ADP
ajst-20808	25	13	markov	markov	NOUN
ajst-20808	25	14	decision	decision	NOUN
ajst-20808	25	15	processes	process	NOUN
ajst-20808	25	16	(	(	PUNCT
ajst-20808	25	17	mdps)[2	mdps)[2	NOUN
ajst-20808	25	18	]	]	PUNCT
ajst-20808	25	19	,	,	PUNCT
ajst-20808	25	20	describing	describe	VERB
ajst-20808	25	21	the	the	DET
ajst-20808	25	22	interaction	interaction	NOUN
ajst-20808	25	23	between	between	ADP
ajst-20808	25	24	the	the	DET
ajst-20808	25	25	agent	agent	NOUN
ajst-20808	25	26	(	(	PUNCT
ajst-20808	25	27	change	change	NOUN
ajst-20808	25	28	detection	detection	NOUN
ajst-20808	25	29	model	model	NOUN
ajst-20808	25	30	)	)	PUNCT
ajst-20808	25	31	and	and	CCONJ
ajst-20808	25	32	the	the	DET
ajst-20808	25	33	environment	environment	NOUN
ajst-20808	25	34	in	in	ADP
ajst-20808	25	35	image	image	NOUN
ajst-20808	25	36	states	state	NOUN
ajst-20808	25	37	.	.	PUNCT
ajst-20808	26	1	here	here	ADV
ajst-20808	26	2	,	,	PUNCT
ajst-20808	26	3	states	state	NOUN
ajst-20808	26	4	represent	represent	VERB
ajst-20808	26	5	specific	specific	ADJ
ajst-20808	26	6	conditions	condition	NOUN
ajst-20808	26	7	of	of	ADP
ajst-20808	26	8	the	the	DET
ajst-20808	26	9	images	image	NOUN
ajst-20808	26	10	,	,	PUNCT
ajst-20808	26	11	actions	action	NOUN
ajst-20808	26	12	denote	denote	VERB
ajst-20808	26	13	operations	operation	NOUN
ajst-20808	26	14	taken	take	VERB
ajst-20808	26	15	by	by	ADP
ajst-20808	26	16	the	the	DET
ajst-20808	26	17	model	model	NOUN
ajst-20808	26	18	in	in	ADP
ajst-20808	26	19	different	different	ADJ
ajst-20808	26	20	states	state	NOUN
ajst-20808	26	21	,	,	PUNCT
ajst-20808	26	22	rewards	reward	NOUN
ajst-20808	26	23	signify	signify	VERB
ajst-20808	26	24	feedback	feedback	NOUN
ajst-20808	26	25	received	receive	VERB
ajst-20808	26	26	from	from	ADP
ajst-20808	26	27	the	the	DET
ajst-20808	26	28	environment	environment	NOUN
ajst-20808	26	29	after	after	ADP
ajst-20808	26	30	performing	perform	VERB
ajst-20808	26	31	actions	action	NOUN
ajst-20808	26	32	,	,	PUNCT
ajst-20808	26	33	and	and	CCONJ
ajst-20808	26	34	policies	policy	NOUN
ajst-20808	26	35	denote	denote	VERB
ajst-20808	26	36	the	the	DET
ajst-20808	26	37	model	model	NOUN
ajst-20808	26	38	's	's	PART
ajst-20808	26	39	strategy	strategy	NOUN
ajst-20808	26	40	for	for	ADP
ajst-20808	26	41	selecting	select	VERB
ajst-20808	26	42	168	168	NUM
ajst-20808	26	43	actions	action	NOUN
ajst-20808	26	44	given	give	VERB
ajst-20808	26	45	states	state	NOUN
ajst-20808	26	46	,	,	PUNCT
ajst-20808	26	47	thereby	thereby	ADV
ajst-20808	26	48	optimizing	optimize	VERB
ajst-20808	26	49	the	the	DET
ajst-20808	26	50	performance	performance	NOUN
ajst-20808	26	51	of	of	ADP
ajst-20808	26	52	the	the	DET
ajst-20808	26	53	change	change	NOUN
ajst-20808	26	54	detection	detection	NOUN
ajst-20808	26	55	model	model	NOUN
ajst-20808	26	56	.	.	PUNCT
ajst-20808	27	1	in	in	ADP
ajst-20808	27	2	reinforcement	reinforcement	NOUN
ajst-20808	27	3	learning	learning	NOUN
ajst-20808	27	4	,	,	PUNCT
ajst-20808	27	5	the	the	DET
ajst-20808	27	6	goal	goal	NOUN
ajst-20808	27	7	of	of	ADP
ajst-20808	27	8	the	the	DET
ajst-20808	27	9	intelligent	intelligent	ADJ
ajst-20808	27	10	body	body	NOUN
ajst-20808	27	11	is	be	AUX
ajst-20808	27	12	to	to	PART
ajst-20808	27	13	learn	learn	VERB
ajst-20808	27	14	an	an	DET
ajst-20808	27	15	optimal	optimal	ADJ
ajst-20808	27	16	policy	policy	NOUN
ajst-20808	27	17	that	that	PRON
ajst-20808	27	18	maximizes	maximize	VERB
ajst-20808	27	19	the	the	DET
ajst-20808	27	20	cumulative	cumulative	ADJ
ajst-20808	27	21	reward	reward	NOUN
ajst-20808	27	22	through	through	ADP
ajst-20808	27	23	interaction	interaction	NOUN
ajst-20808	27	24	with	with	ADP
ajst-20808	27	25	the	the	DET
ajst-20808	27	26	environment	environment	NOUN
ajst-20808	27	27	.	.	PUNCT
ajst-20808	28	1	to	to	PART
ajst-20808	28	2	achieve	achieve	VERB
ajst-20808	28	3	this	this	DET
ajst-20808	28	4	goal	goal	NOUN
ajst-20808	28	5	,	,	PUNCT
ajst-20808	28	6	the	the	DET
ajst-20808	28	7	intelligent	intelligent	ADJ
ajst-20808	28	8	body	body	NOUN
ajst-20808	28	9	needs	need	VERB
ajst-20808	28	10	to	to	PART
ajst-20808	28	11	explore	explore	VERB
ajst-20808	28	12	different	different	ADJ
ajst-20808	28	13	strategies	strategy	NOUN
ajst-20808	28	14	through	through	ADP
ajst-20808	28	15	trial	trial	NOUN
ajst-20808	28	16	and	and	CCONJ
ajst-20808	28	17	error	error	NOUN
ajst-20808	28	18	and	and	CCONJ
ajst-20808	28	19	guide	guide	VERB
ajst-20808	28	20	the	the	DET
ajst-20808	28	21	learning	learning	NOUN
ajst-20808	28	22	process	process	NOUN
ajst-20808	28	23	through	through	ADP
ajst-20808	28	24	reward	reward	NOUN
ajst-20808	28	25	signals	signal	NOUN
ajst-20808	28	26	.	.	PUNCT
ajst-20808	29	1	just	just	ADV
ajst-20808	29	2	as	as	SCONJ
ajst-20808	29	3	mentioned	mention	VERB
ajst-20808	29	4	in	in	ADP
ajst-20808	29	5	the	the	DET
ajst-20808	29	6	"	"	PUNCT
ajst-20808	29	7	detect	detect	VERB
ajst-20808	29	8	any	any	DET
ajst-20808	29	9	deepfakes	deepfake	NOUN
ajst-20808	29	10	(	(	PUNCT
ajst-20808	29	11	dadf	dadf	NOUN
ajst-20808	29	12	)	)	PUNCT
ajst-20808	29	13	"	"	PUNCT
ajst-20808	29	14	framework	framework	NOUN
ajst-20808	29	15	,	,	PUNCT
ajst-20808	29	16	the	the	DET
ajst-20808	29	17	reconstruction	reconstruction	NOUN
ajst-20808	29	18	guided	guide	VERB
ajst-20808	29	19	attention	attention	NOUN
ajst-20808	29	20	(	(	PUNCT
ajst-20808	29	21	rga	rga	NOUN
ajst-20808	29	22	)	)	PUNCT
ajst-20808	29	23	module	module	NOUN
ajst-20808	29	24	is	be	AUX
ajst-20808	29	25	introduced	introduce	VERB
ajst-20808	29	26	to	to	PART
ajst-20808	29	27	better	well	ADV
ajst-20808	29	28	identify	identify	VERB
ajst-20808	29	29	forged	forge	VERB
ajst-20808	29	30	traces	trace	NOUN
ajst-20808	29	31	and	and	CCONJ
ajst-20808	29	32	enhance	enhance	VERB
ajst-20808	29	33	the	the	DET
ajst-20808	29	34	model	model	NOUN
ajst-20808	29	35	's	's	PART
ajst-20808	29	36	sensitivity	sensitivity	NOUN
ajst-20808	29	37	to	to	PART
ajst-20808	29	38	manipulated	manipulated	VERB
ajst-20808	29	39	areas[3	areas[3	PROPN
ajst-20808	29	40	]	]	X
ajst-20808	29	41	.	.	PUNCT
ajst-20808	30	1	such	such	DET
ajst-20808	30	2	a	a	DET
ajst-20808	30	3	framework	framework	NOUN
ajst-20808	30	4	seamlessly	seamlessly	ADV
ajst-20808	30	5	integrates	integrate	VERB
ajst-20808	30	6	end	end	NOUN
ajst-20808	30	7	-	-	PUNCT
ajst-20808	30	8	to	to	ADP
ajst-20808	30	9	-	-	PUNCT
ajst-20808	30	10	end	end	NOUN
ajst-20808	30	11	forgery	forgery	NOUN
ajst-20808	30	12	localization	localization	NOUN
ajst-20808	30	13	and	and	CCONJ
ajst-20808	30	14	detection	detection	NOUN
ajst-20808	30	15	optimization	optimization	NOUN
ajst-20808	30	16	,	,	PUNCT
ajst-20808	30	17	allowing	allow	VERB
ajst-20808	30	18	the	the	DET
ajst-20808	30	19	agent	agent	NOUN
ajst-20808	30	20	to	to	PART
ajst-20808	30	21	progressively	progressively	ADV
ajst-20808	30	22	optimize	optimize	VERB
ajst-20808	30	23	its	its	PRON
ajst-20808	30	24	behavioral	behavioral	ADJ
ajst-20808	30	25	strategies	strategy	NOUN
ajst-20808	30	26	through	through	ADP
ajst-20808	30	27	continuous	continuous	ADJ
ajst-20808	30	28	experimentation	experimentation	NOUN
ajst-20808	30	29	and	and	CCONJ
ajst-20808	30	30	feedback	feedback	NOUN
ajst-20808	30	31	,	,	PUNCT
ajst-20808	30	32	thereby	thereby	ADV
ajst-20808	30	33	adapting	adapt	VERB
ajst-20808	30	34	to	to	ADP
ajst-20808	30	35	various	various	ADJ
ajst-20808	30	36	environments	environment	NOUN
ajst-20808	30	37	and	and	CCONJ
ajst-20808	30	38	tasks	task	NOUN
ajst-20808	30	39	.	.	PUNCT
ajst-20808	31	1	reinforcement	reinforcement	NOUN
ajst-20808	31	2	learning	learning	NOUN
ajst-20808	31	3	methods	method	NOUN
ajst-20808	31	4	usually	usually	ADV
ajst-20808	31	5	include	include	VERB
ajst-20808	31	6	key	key	ADJ
ajst-20808	31	7	techniques	technique	NOUN
ajst-20808	31	8	such	such	ADJ
ajst-20808	31	9	as	as	ADP
ajst-20808	31	10	value	value	NOUN
ajst-20808	31	11	function	function	NOUN
ajst-20808	31	12	estimation	estimation	NOUN
ajst-20808	31	13	,	,	PUNCT
ajst-20808	31	14	strategy	strategy	NOUN
ajst-20808	31	15	optimization	optimization	NOUN
ajst-20808	31	16	,	,	PUNCT
ajst-20808	31	17	and	and	CCONJ
ajst-20808	31	18	exploration	exploration	NOUN
ajst-20808	31	19	and	and	CCONJ
ajst-20808	31	20	exploitation	exploitation	NOUN
ajst-20808	31	21	.	.	PUNCT
ajst-20808	32	1	through	through	ADP
ajst-20808	32	2	these	these	DET
ajst-20808	32	3	techniques	technique	NOUN
ajst-20808	32	4	,	,	PUNCT
ajst-20808	32	5	intelligent	intelligent	ADJ
ajst-20808	32	6	bodies	body	NOUN
ajst-20808	32	7	can	can	AUX
ajst-20808	32	8	effectively	effectively	ADV
ajst-20808	32	9	learn	learn	VERB
ajst-20808	32	10	and	and	CCONJ
ajst-20808	32	11	make	make	VERB
ajst-20808	32	12	decisions	decision	NOUN
ajst-20808	32	13	in	in	ADP
ajst-20808	32	14	complex	complex	ADJ
ajst-20808	32	15	environments	environment	NOUN
ajst-20808	32	16	,	,	PUNCT
ajst-20808	32	17	thus	thus	ADV
ajst-20808	32	18	realizing	realize	VERB
ajst-20808	32	19	the	the	DET
ajst-20808	32	20	goal	goal	NOUN
ajst-20808	32	21	of	of	ADP
ajst-20808	32	22	autonomous	autonomous	ADJ
ajst-20808	32	23	intelligent	intelligent	ADJ
ajst-20808	32	24	behavior	behavior	NOUN
ajst-20808	32	25	.	.	PUNCT
ajst-20808	33	1	in	in	ADP
ajst-20808	33	2	practice	practice	NOUN
ajst-20808	33	3	,	,	PUNCT
ajst-20808	33	4	reinforcement	reinforcement	NOUN
ajst-20808	33	5	learning	learning	NOUN
ajst-20808	33	6	is	be	AUX
ajst-20808	33	7	widely	widely	ADV
ajst-20808	33	8	used	use	VERB
ajst-20808	33	9	in	in	ADP
ajst-20808	33	10	au	au	PROPN
ajst-20808	33	11	tomatic	tomatic	PROPN
ajst-20808	33	12	control	control	NOUN
ajst-20808	33	13	,	,	PUNCT
ajst-20808	33	14	game	game	NOUN
ajst-20808	33	15	design	design	NOUN
ajst-20808	33	16	,	,	PUNCT
ajst-20808	33	17	financial	financial	ADJ
ajst-20808	33	18	trading	trading	NOUN
ajst-20808	33	19	and	and	CCONJ
ajst-20808	33	20	other	other	ADJ
ajst-20808	33	21	fields	field	NOUN
ajst-20808	33	22	,	,	PUNCT
ajst-20808	33	23	and	and	CCONJ
ajst-20808	33	24	plays	play	VERB
ajst-20808	33	25	an	an	DET
ajst-20808	33	26	important	important	ADJ
ajst-20808	33	27	role	role	NOUN
ajst-20808	33	28	in	in	ADP
ajst-20808	33	29	the	the	DET
ajst-20808	33	30	field	field	NOUN
ajst-20808	33	31	of	of	ADP
ajst-20808	33	32	artificia	artificia	NOUN
ajst-20808	33	33	l	l	NOUN
ajst-20808	33	34	intelligence	intelligence	NOUN
ajst-20808	33	35	.	.	PUNCT
ajst-20808	34	1	with	with	ADP
ajst-20808	34	2	the	the	DET
ajst-20808	34	3	development	development	NOUN
ajst-20808	34	4	of	of	ADP
ajst-20808	34	5	deep	deep	ADJ
ajst-20808	34	6	learning	learning	NOUN
ajst-20808	34	7	and	and	CCONJ
ajst-20808	34	8	other	other	ADJ
ajst-20808	34	9	technologies	technology	NOUN
ajst-20808	34	10	,	,	PUNCT
ajst-20808	34	11	reinforcement	reinforcement	NOUN
ajst-20808	34	12	learning	learning	NOUN
ajst-20808	34	13	has	have	VERB
ajst-20808	34	14	great	great	ADJ
ajst-20808	34	15	poten	poten	ADJ
ajst-20808	34	16	tial	tial	NOUN
ajst-20808	34	17	for	for	ADP
ajst-20808	34	18	solving	solve	VERB
ajst-20808	34	19	complex	complex	ADJ
ajst-20808	34	20	tasks	task	NOUN
ajst-20808	34	21	and	and	CCONJ
ajst-20808	34	22	realizing	realize	VERB
ajst-20808	34	23	the	the	DET
ajst-20808	34	24	generalize	generalize	NOUN
ajst-20808	34	25	d	d	ADP
ajst-20808	34	26	goals	goal	NOUN
ajst-20808	34	27	of	of	ADP
ajst-20808	34	28	artificial	artificial	ADJ
ajst-20808	34	29	intelligence	intelligence	NOUN
ajst-20808	34	30	.	.	PUNCT
ajst-20808	35	1	2.2	2.2	NUM
ajst-20808	35	2	.	.	PUNCT
ajst-20808	36	1	classification	classification	NOUN
ajst-20808	36	2	of	of	ADP
ajst-20808	36	3	common	common	ADJ
ajst-20808	36	4	reinforcement	reinforcement	NOUN
ajst-20808	36	5	learning	learning	NOUN
ajst-20808	36	6	algorithms	algorithms	ADJ
ajst-20808	36	7	2.2.1	2.2.1	NUM
ajst-20808	36	8	.	.	PUNCT
ajst-20808	37	1	classification	classification	NOUN
ajst-20808	37	2	based	base	VERB
ajst-20808	37	3	on	on	ADP
ajst-20808	37	4	learning	learn	VERB
ajst-20808	37	5	mode	mode	NOUN
ajst-20808	37	6	reinforcement	reinforcement	NOUN
ajst-20808	37	7	learning	learning	NOUN
ajst-20808	37	8	algorithms	algorithm	NOUN
ajst-20808	37	9	can	can	AUX
ajst-20808	37	10	be	be	AUX
ajst-20808	37	11	classified	classify	VERB
ajst-20808	37	12	into	into	ADP
ajst-20808	37	13	two	two	NUM
ajst-20808	37	14	main	main	ADJ
ajst-20808	37	15	types	type	NOUN
ajst-20808	37	16	according	accord	VERB
ajst-20808	37	17	to	to	ADP
ajst-20808	37	18	how	how	SCONJ
ajst-20808	37	19	the	the	DET
ajst-20808	37	20	intelligences	intelligence	NOUN
ajst-20808	37	21	learn	learn	VERB
ajst-20808	37	22	and	and	CCONJ
ajst-20808	37	23	optimize	optimize	VERB
ajst-20808	37	24	their	their	PRON
ajst-20808	37	25	strategies	strategy	NOUN
ajst-20808	37	26	:	:	PUNCT
ajst-20808	37	27	value	value	NOUN
ajst-20808	37	28	function	function	NOUN
ajst-20808	37	29	-	-	PUNCT
ajst-20808	37	30	based	base	VERB
ajst-20808	37	31	methods	method	NOUN
ajst-20808	37	32	and	and	CCONJ
ajst-20808	37	33	strategy	strategy	NOUN
ajst-20808	37	34	gradient	gradient	NOUN
ajst-20808	37	35	-	-	PUNCT
ajst-20808	37	36	based	base	VERB
ajst-20808	37	37	methods	method	NOUN
ajst-20808	37	38	.	.	PUNCT
ajst-20808	38	1	value	value	NOUN
ajst-20808	38	2	function	function	NOUN
ajst-20808	38	3	-	-	PUNCT
ajst-20808	38	4	based	base	VERB
ajst-20808	38	5	methods	method	NOUN
ajst-20808	38	6	:	:	PUNCT
ajst-20808	38	7	these	these	DET
ajst-20808	38	8	algorithms	algorithm	NOUN
ajst-20808	38	9	optimize	optimize	VERB
ajst-20808	38	10	strategies	strategy	NOUN
ajst-20808	38	11	by	by	ADP
ajst-20808	38	12	estimating	estimate	VERB
ajst-20808	38	13	the	the	DET
ajst-20808	38	14	value	value	NOUN
ajst-20808	38	15	function	function	NOUN
ajst-20808	38	16	of	of	ADP
ajst-20808	38	17	states	state	NOUN
ajst-20808	38	18	or	or	CCONJ
ajst-20808	38	19	stateaction	stateaction	NOUN
ajst-20808	38	20	pairs	pair	NOUN
ajst-20808	38	21	.	.	PUNCT
ajst-20808	39	1	among	among	ADP
ajst-20808	39	2	them	they	PRON
ajst-20808	39	3	,	,	PUNCT
ajst-20808	39	4	q	q	NOUN
ajst-20808	39	5	-	-	PUNCT
ajst-20808	39	6	learning	learning	NOUN
ajst-20808	39	7	and	and	CCONJ
ajst-20808	39	8	sarsa	sarsa	NOUN
ajst-20808	39	9	are	be	AUX
ajst-20808	39	10	the	the	DET
ajst-20808	39	11	most	most	ADV
ajst-20808	39	12	classical	classical	ADJ
ajst-20808	39	13	value	value	NOUN
ajst-20808	39	14	function	function	NOUN
ajst-20808	39	15	-	-	PUNCT
ajst-20808	39	16	based	base	VERB
ajst-20808	39	17	algorithms	algorithm	NOUN
ajst-20808	39	18	that	that	PRON
ajst-20808	39	19	improve	improve	VERB
ajst-20808	39	20	the	the	DET
ajst-20808	39	21	policy	policy	NOUN
ajst-20808	39	22	by	by	ADP
ajst-20808	39	23	updating	update	VERB
ajst-20808	39	24	the	the	DET
ajst-20808	39	25	state	state	NOUN
ajst-20808	39	26	-	-	PUNCT
ajst-20808	39	27	action	action	NOUN
ajst-20808	39	28	value	value	NOUN
ajst-20808	39	29	function	function	NOUN
ajst-20808	39	30	.	.	PUNCT
ajst-20808	40	1	in	in	ADP
ajst-20808	40	2	addition	addition	NOUN
ajst-20808	40	3	,	,	PUNCT
ajst-20808	40	4	there	there	PRON
ajst-20808	40	5	are	be	VERB
ajst-20808	40	6	value	value	NOUN
ajst-20808	40	7	-	-	PUNCT
ajst-20808	40	8	based	base	VERB
ajst-20808	40	9	approximation	approximation	NOUN
ajst-20808	40	10	methods	method	NOUN
ajst-20808	40	11	such	such	ADJ
ajst-20808	40	12	as	as	ADP
ajst-20808	40	13	dqn	dqn	NOUN
ajst-20808	40	14	(	(	PUNCT
ajst-20808	40	15	deep	deep	ADJ
ajst-20808	40	16	q	q	NOUN
ajst-20808	40	17	networks	network	NOUN
ajst-20808	40	18	)	)	PUNCT
ajst-20808	40	19	,	,	PUNCT
ajst-20808	40	20	which	which	PRON
ajst-20808	40	21	utilize	utilize	VERB
ajst-20808	40	22	neural	neural	ADJ
ajst-20808	40	23	networks	network	NOUN
ajst-20808	40	24	to	to	PART
ajst-20808	40	25	approximate	approximate	VERB
ajst-20808	40	26	the	the	DET
ajst-20808	40	27	value	value	NOUN
ajst-20808	40	28	function	function	NOUN
ajst-20808	40	29	to	to	PART
ajst-20808	40	30	deal	deal	VERB
ajst-20808	40	31	with	with	ADP
ajst-20808	40	32	continuous	continuous	ADJ
ajst-20808	40	33	state	state	NOUN
ajst-20808	40	34	space	space	NOUN
ajst-20808	40	35	problems	problem	NOUN
ajst-20808	40	36	.	.	PUNCT
ajst-20808	41	1	policy	policy	NOUN
ajst-20808	41	2	gradient	gradient	NOUN
ajst-20808	41	3	based	base	VERB
ajst-20808	41	4	methods	method	NOUN
ajst-20808	41	5	:	:	PUNCT
ajst-20808	41	6	these	these	DET
ajst-20808	41	7	algorithms	algorithm	NOUN
ajst-20808	41	8	learn	learn	VERB
ajst-20808	41	9	the	the	DET
ajst-20808	41	10	policy	policy	NOUN
ajst-20808	41	11	function	function	NOUN
ajst-20808	41	12	directly	directly	ADV
ajst-20808	41	13	without	without	ADP
ajst-20808	41	14	estimating	estimate	VERB
ajst-20808	41	15	the	the	DET
ajst-20808	41	16	value	value	NOUN
ajst-20808	41	17	function	function	NOUN
ajst-20808	41	18	.	.	PUNCT
ajst-20808	42	1	they	they	PRON
ajst-20808	42	2	update	update	VERB
ajst-20808	42	3	the	the	DET
ajst-20808	42	4	policy	policy	NOUN
ajst-20808	42	5	parameters	parameter	NOUN
ajst-20808	42	6	by	by	ADP
ajst-20808	42	7	maximizing	maximize	VERB
ajst-20808	42	8	the	the	DET
ajst-20808	42	9	gradient	gradient	NOUN
ajst-20808	42	10	of	of	ADP
ajst-20808	42	11	the	the	DET
ajst-20808	42	12	cumulative	cumulative	ADJ
ajst-20808	42	13	reward	reward	NOUN
ajst-20808	42	14	.	.	PUNCT
ajst-20808	43	1	typical	typical	ADJ
ajst-20808	43	2	policy	policy	NOUN
ajst-20808	43	3	gradient	gradient	NOUN
ajst-20808	43	4	based	base	VERB
ajst-20808	43	5	algorithms	algorithm	NOUN
ajst-20808	43	6	include	include	VERB
ajst-20808	43	7	reinforce	reinforce	NOUN
ajst-20808	43	8	,	,	PUNCT
ajst-20808	43	9	ppo	ppo	PROPN
ajst-20808	43	10	(	(	PUNCT
ajst-20808	43	11	proximal	proximal	ADJ
ajst-20808	43	12	policy	policy	NOUN
ajst-20808	43	13	optimization	optimization	NOUN
ajst-20808	43	14	)	)	PUNCT
ajst-20808	43	15	,	,	PUNCT
ajst-20808	43	16	trpo	trpo	NOUN
ajst-20808	43	17	(	(	PUNCT
ajst-20808	43	18	relative	relative	ADJ
ajst-20808	43	19	entropy	entropy	NOUN
ajst-20808	43	20	policy	policy	NOUN
ajst-20808	43	21	optimization	optimization	NOUN
ajst-20808	43	22	)	)	PUNCT
ajst-20808	43	23	,	,	PUNCT
ajst-20808	43	24	etc	etc	X
ajst-20808	43	25	.	.	X
ajst-20808	44	1	these	these	DET
ajst-20808	44	2	algorithms	algorithm	NOUN
ajst-20808	44	3	have	have	VERB
ajst-20808	44	4	better	well	ADJ
ajst-20808	44	5	performance	performance	NOUN
ajst-20808	44	6	and	and	CCONJ
ajst-20808	44	7	generalization	generalization	NOUN
ajst-20808	44	8	ability	ability	NOUN
ajst-20808	44	9	in	in	ADP
ajst-20808	44	10	dealing	deal	VERB
ajst-20808	44	11	with	with	ADP
ajst-20808	44	12	problems	problem	NOUN
ajst-20808	44	13	in	in	ADP
ajst-20808	44	14	continuous	continuous	ADJ
ajst-20808	44	15	action	action	NOUN
ajst-20808	44	16	space	space	NOUN
ajst-20808	44	17	.	.	PUNCT
ajst-20808	45	1	2.2.2	2.2.2	X
ajst-20808	45	2	.	.	X
ajst-20808	45	3	classification	classification	NOUN
ajst-20808	45	4	based	base	VERB
ajst-20808	45	5	on	on	ADP
ajst-20808	45	6	policy	policy	NOUN
ajst-20808	45	7	updating	update	VERB
ajst-20808	45	8	method	method	NOUN
ajst-20808	45	9	offline	offline	ADJ
ajst-20808	45	10	updating	update	VERB
ajst-20808	45	11	algorithms	algorithm	NOUN
ajst-20808	45	12	:	:	PUNCT
ajst-20808	45	13	these	these	DET
ajst-20808	45	14	algorithms	algorithm	NOUN
ajst-20808	45	15	use	use	VERB
ajst-20808	45	16	the	the	DET
ajst-20808	45	17	experience	experience	NOUN
ajst-20808	45	18	of	of	ADP
ajst-20808	45	19	the	the	DET
ajst-20808	45	20	whole	whole	ADJ
ajst-20808	45	21	round	round	NOUN
ajst-20808	45	22	to	to	PART
ajst-20808	45	23	update	update	VERB
ajst-20808	45	24	the	the	DET
ajst-20808	45	25	strategy	strategy	NOUN
ajst-20808	45	26	parameters	parameter	NOUN
ajst-20808	45	27	after	after	SCONJ
ajst-20808	45	28	the	the	DET
ajst-20808	45	29	complete	complete	ADJ
ajst-20808	45	30	round	round	NOUN
ajst-20808	45	31	is	be	AUX
ajst-20808	45	32	over	over	ADV
ajst-20808	45	33	,	,	PUNCT
ajst-20808	45	34	q	q	NOUN
ajst-20808	45	35	-	-	PUNCT
ajst-20808	45	36	learning	learning	NOUN
ajst-20808	45	37	and	and	CCONJ
ajst-20808	45	38	sarsa	sarsa	NOUN
ajst-20808	45	39	are	be	AUX
ajst-20808	45	40	representatives	representative	NOUN
ajst-20808	45	41	of	of	ADP
ajst-20808	45	42	offline	offline	ADJ
ajst-20808	45	43	updating	update	VERB
ajst-20808	45	44	algorithms	algorithm	NOUN
ajst-20808	45	45	.	.	PUNCT
ajst-20808	46	1	online	online	ADJ
ajst-20808	46	2	updating	update	VERB
ajst-20808	46	3	algorithms	algorithm	NOUN
ajst-20808	46	4	:	:	PUNCT
ajst-20808	46	5	these	these	DET
ajst-20808	46	6	algorithms	algorithm	NOUN
ajst-20808	46	7	update	update	VERB
ajst-20808	46	8	the	the	DET
ajst-20808	46	9	policy	policy	NOUN
ajst-20808	46	10	parameters	parameter	NOUN
ajst-20808	46	11	at	at	ADP
ajst-20808	46	12	each	each	DET
ajst-20808	46	13	step	step	NOUN
ajst-20808	46	14	or	or	CCONJ
ajst-20808	46	15	time	time	NOUN
ajst-20808	46	16	step	step	NOUN
ajst-20808	46	17	without	without	ADP
ajst-20808	46	18	waiting	wait	VERB
ajst-20808	46	19	until	until	ADP
ajst-20808	46	20	the	the	DET
ajst-20808	46	21	end	end	NOUN
ajst-20808	46	22	of	of	ADP
ajst-20808	46	23	the	the	DET
ajst-20808	46	24	complete	complete	ADJ
ajst-20808	46	25	round	round	NOUN
ajst-20808	46	26	.	.	PUNCT
ajst-20808	47	1	reinforce	reinforce	NOUN
ajst-20808	47	2	and	and	CCONJ
ajst-20808	47	3	ddpg	ddpg	ADJ
ajst-20808	47	4	(	(	PUNCT
ajst-20808	47	5	deep	deep	ADJ
ajst-20808	47	6	deterministic	deterministic	ADJ
ajst-20808	47	7	policy	policy	NOUN
ajst-20808	47	8	gradient	gradient	NOUN
ajst-20808	47	9	)	)	PUNCT
ajst-20808	47	10	are	be	AUX
ajst-20808	47	11	typical	typical	ADJ
ajst-20808	47	12	online	online	ADJ
ajst-20808	47	13	updating	update	VERB
ajst-20808	47	14	algorithms	algorithm	NOUN
ajst-20808	47	15	that	that	PRON
ajst-20808	47	16	are	be	AUX
ajst-20808	47	17	able	able	ADJ
ajst-20808	47	18	to	to	PART
ajst-20808	47	19	adapt	adapt	VERB
ajst-20808	47	20	to	to	ADP
ajst-20808	47	21	continuous	continuous	ADJ
ajst-20808	47	22	environments	environment	NOUN
ajst-20808	47	23	and	and	CCONJ
ajst-20808	47	24	update	update	VERB
ajst-20808	47	25	the	the	DET
ajst-20808	47	26	policy	policy	NOUN
ajst-20808	47	27	in	in	ADP
ajst-20808	47	28	real	real	ADJ
ajst-20808	47	29	time	time	NOUN
ajst-20808	47	30	.	.	PUNCT
ajst-20808	48	1	2.2.3	2.2.3	X
ajst-20808	48	2	.	.	X
ajst-20808	49	1	classification	classification	NOUN
ajst-20808	49	2	based	base	VERB
ajst-20808	49	3	on	on	ADP
ajst-20808	49	4	value	value	NOUN
ajst-20808	49	5	function	function	NOUN
ajst-20808	49	6	estimation	estimation	NOUN
ajst-20808	49	7	approach	approach	NOUN
ajst-20808	49	8	exact	exact	ADJ
ajst-20808	49	9	value	value	NOUN
ajst-20808	49	10	function	function	NOUN
ajst-20808	49	11	estimation	estimation	NOUN
ajst-20808	49	12	algorithms	algorithm	NOUN
ajst-20808	49	13	:	:	PUNCT
ajst-20808	49	14	these	these	DET
ajst-20808	49	15	algorithms	algorithm	NOUN
ajst-20808	49	16	attempt	attempt	VERB
ajst-20808	49	17	to	to	PART
ajst-20808	49	18	directly	directly	ADV
ajst-20808	49	19	estimate	estimate	VERB
ajst-20808	49	20	an	an	DET
ajst-20808	49	21	exact	exact	ADJ
ajst-20808	49	22	value	value	NOUN
ajst-20808	49	23	function	function	NOUN
ajst-20808	49	24	,	,	PUNCT
ajst-20808	49	25	such	such	ADJ
ajst-20808	49	26	as	as	ADP
ajst-20808	49	27	an	an	DET
ajst-20808	49	28	exact	exact	ADJ
ajst-20808	49	29	state	state	NOUN
ajst-20808	49	30	value	value	NOUN
ajst-20808	49	31	function	function	NOUN
ajst-20808	49	32	or	or	CCONJ
ajst-20808	49	33	state	state	NOUN
ajst-20808	49	34	action	action	NOUN
ajst-20808	49	35	value	value	NOUN
ajst-20808	49	36	function	function	NOUN
ajst-20808	49	37	.	.	PUNCT
ajst-20808	50	1	typical	typical	ADJ
ajst-20808	50	2	algorithms	algorithm	NOUN
ajst-20808	50	3	include	include	VERB
ajst-20808	50	4	q	q	ADJ
ajst-20808	50	5	-	-	PUNCT
ajst-20808	50	6	learning	learning	NOUN
ajst-20808	50	7	and	and	CCONJ
ajst-20808	50	8	sarsa	sarsa	PROPN
ajst-20808	50	9	.	.	PUNCT
ajst-20808	51	1	approximate	approximate	ADJ
ajst-20808	51	2	value	value	NOUN
ajst-20808	51	3	function	function	NOUN
ajst-20808	51	4	estimation	estimation	NOUN
ajst-20808	51	5	algorithms	algorithm	NOUN
ajst-20808	51	6	:	:	PUNCT
ajst-20808	51	7	these	these	DET
ajst-20808	51	8	algorithms	algorithm	NOUN
ajst-20808	51	9	use	use	VERB
ajst-20808	51	10	function	function	NOUN
ajst-20808	51	11	approximators	approximator	NOUN
ajst-20808	51	12	(	(	PUNCT
ajst-20808	51	13	e.g.	e.g.	ADV
ajst-20808	51	14	,	,	PUNCT
ajst-20808	51	15	neural	neural	ADJ
ajst-20808	51	16	networks	network	NOUN
ajst-20808	51	17	)	)	PUNCT
ajst-20808	51	18	to	to	PART
ajst-20808	51	19	approximate	approximate	VERB
ajst-20808	51	20	the	the	DET
ajst-20808	51	21	value	value	NOUN
ajst-20808	51	22	function	function	NOUN
ajst-20808	51	23	.	.	PUNCT
ajst-20808	52	1	these	these	DET
ajst-20808	52	2	algorithms	algorithm	NOUN
ajst-20808	52	3	are	be	AUX
ajst-20808	52	4	capable	capable	ADJ
ajst-20808	52	5	of	of	ADP
ajst-20808	52	6	handling	handle	VERB
ajst-20808	52	7	large	large	ADJ
ajst-20808	52	8	-	-	PUNCT
ajst-20808	52	9	scale	scale	NOUN
ajst-20808	52	10	state	state	NOUN
ajst-20808	52	11	-	-	PUNCT
ajst-20808	52	12	space	space	NOUN
ajst-20808	52	13	and	and	CCONJ
ajst-20808	52	14	action	action	NOUN
ajst-20808	52	15	-	-	PUNCT
ajst-20808	52	16	space	space	NOUN
ajst-20808	52	17	problems	problem	NOUN
ajst-20808	52	18	and	and	CCONJ
ajst-20808	52	19	have	have	AUX
ajst-20808	52	20	performed	perform	VERB
ajst-20808	52	21	well	well	ADV
ajst-20808	52	22	in	in	ADP
ajst-20808	52	23	practice	practice	NOUN
ajst-20808	52	24	.	.	PUNCT
ajst-20808	53	1	for	for	ADP
ajst-20808	53	2	example	example	NOUN
ajst-20808	53	3	,	,	PUNCT
ajst-20808	53	4	dqn	dqn	NOUN
ajst-20808	53	5	utilizes	utilize	VERB
ajst-20808	53	6	deep	deep	ADJ
ajst-20808	53	7	neural	neural	ADJ
ajst-20808	53	8	networks	network	NOUN
ajst-20808	53	9	to	to	PART
ajst-20808	53	10	approximate	approximate	VERB
ajst-20808	53	11	the	the	DET
ajst-20808	53	12	state	state	NOUN
ajst-20808	53	13	-	-	PUNCT
ajst-20808	53	14	action	action	NOUN
ajst-20808	53	15	value	value	NOUN
ajst-20808	53	16	function	function	NOUN
ajst-20808	53	17	,	,	PUNCT
ajst-20808	53	18	thus	thus	ADV
ajst-20808	53	19	enabling	enable	VERB
ajst-20808	53	20	efficient	efficient	ADJ
ajst-20808	53	21	estimation	estimation	NOUN
ajst-20808	53	22	of	of	ADP
ajst-20808	53	23	continuous	continuous	ADJ
ajst-20808	53	24	state	state	NOUN
ajst-20808	53	25	spaces.in	spaces.in	X
ajst-20808	53	26	the	the	DET
ajst-20808	53	27	field	field	NOUN
ajst-20808	53	28	of	of	ADP
ajst-20808	53	29	digital	digital	ADJ
ajst-20808	53	30	asset	asset	NOUN
ajst-20808	53	31	trading	trading	NOUN
ajst-20808	53	32	,	,	PUNCT
ajst-20808	53	33	researchers	researcher	NOUN
ajst-20808	53	34	have	have	AUX
ajst-20808	53	35	begun	begin	VERB
ajst-20808	53	36	exploring	explore	VERB
ajst-20808	53	37	the	the	DET
ajst-20808	53	38	integration	integration	NOUN
ajst-20808	53	39	of	of	ADP
ajst-20808	53	40	distributed	distribute	VERB
ajst-20808	53	41	ledger	ledger	NOUN
ajst-20808	53	42	technology	technology	NOUN
ajst-20808	53	43	(	(	PUNCT
ajst-20808	53	44	dlt	dlt	PROPN
ajst-20808	53	45	)	)	PUNCT
ajst-20808	53	46	and	and	CCONJ
ajst-20808	53	47	dqn	dqn	NOUN
ajst-20808	53	48	to	to	PART
ajst-20808	53	49	further	far	ADV
ajst-20808	53	50	enhance	enhance	VERB
ajst-20808	53	51	the	the	DET
ajst-20808	53	52	security	security	NOUN
ajst-20808	53	53	and	and	CCONJ
ajst-20808	53	54	reliability	reliability	NOUN
ajst-20808	53	55	of	of	ADP
ajst-20808	53	56	transactions	transaction	NOUN
ajst-20808	53	57	.	.	PUNCT
ajst-20808	54	1	this	this	DET
ajst-20808	54	2	integration	integration	NOUN
ajst-20808	54	3	lays	lay	VERB
ajst-20808	54	4	a	a	DET
ajst-20808	54	5	solid	solid	ADJ
ajst-20808	54	6	foundation	foundation	NOUN
ajst-20808	54	7	for	for	ADP
ajst-20808	54	8	the	the	DET
ajst-20808	54	9	development	development	NOUN
ajst-20808	54	10	of	of	ADP
ajst-20808	54	11	digital	digital	ADJ
ajst-20808	54	12	asset	asset	NOUN
ajst-20808	54	13	trading[4	trading[4	NUM
ajst-20808	54	14	]	]	PUNCT
ajst-20808	54	15	.	.	PUNCT
ajst-20808	55	1	in	in	ADP
ajst-20808	55	2	this	this	DET
ajst-20808	55	3	process	process	NOUN
ajst-20808	55	4	,	,	PUNCT
ajst-20808	55	5	reinforcement	reinforcement	NOUN
ajst-20808	55	6	learning	learning	NOUN
ajst-20808	55	7	,	,	PUNCT
ajst-20808	55	8	as	as	ADP
ajst-20808	55	9	a	a	DET
ajst-20808	55	10	learning	learning	NOUN
ajst-20808	55	11	paradigm	paradigm	NOUN
ajst-20808	55	12	based	base	VERB
ajst-20808	55	13	on	on	ADP
ajst-20808	55	14	the	the	DET
ajst-20808	55	15	interaction	interaction	NOUN
ajst-20808	55	16	between	between	ADP
ajst-20808	55	17	agents	agent	NOUN
ajst-20808	55	18	and	and	CCONJ
ajst-20808	55	19	the	the	DET
ajst-20808	55	20	environment	environment	NOUN
ajst-20808	55	21	,	,	PUNCT
ajst-20808	55	22	also	also	ADV
ajst-20808	55	23	plays	play	VERB
ajst-20808	55	24	a	a	DET
ajst-20808	55	25	crucial	crucial	ADJ
ajst-20808	55	26	role	role	NOUN
ajst-20808	55	27	.	.	PUNCT
ajst-20808	56	1	3	3	X
ajst-20808	56	2	.	.	X
ajst-20808	56	3	evaluation	evaluation	NOUN
ajst-20808	56	4	criteria	criterion	NOUN
ajst-20808	56	5	for	for	ADP
ajst-20808	56	6	reinforcement	reinforcement	NOUN
ajst-20808	56	7	learning	learning	NOUN
ajst-20808	56	8	methods	method	NOUN
ajst-20808	56	9	3.1	3.1	NUM
ajst-20808	56	10	.	.	PUNCT
ajst-20808	57	1	performance	performance	NOUN
ajst-20808	57	2	evaluation	evaluation	NOUN
ajst-20808	57	3	metrics	metric	NOUN
ajst-20808	57	4	3.1.1	3.1.1	NUM
ajst-20808	57	5	.	.	PUNCT
ajst-20808	58	1	cumulative	cumulative	ADJ
ajst-20808	58	2	reward	reward	NOUN
ajst-20808	58	3	cumulative	cumulative	ADJ
ajst-20808	58	4	reward	reward	NOUN
ajst-20808	58	5	is	be	AUX
ajst-20808	58	6	one	one	NUM
ajst-20808	58	7	of	of	ADP
ajst-20808	58	8	the	the	DET
ajst-20808	58	9	core	core	NOUN
ajst-20808	58	10	evaluation	evaluation	NOUN
ajst-20808	58	11	indexes	index	NOUN
ajst-20808	58	12	of	of	ADP
ajst-20808	58	13	the	the	DET
ajst-20808	58	14	reinforcement	reinforcement	NOUN
ajst-20808	58	15	learning	learning	NOUN
ajst-20808	58	16	algorithm	algorithm	NOUN
ajst-20808	58	17	,	,	PUNCT
ajst-20808	58	18	representing	represent	VERB
ajst-20808	58	19	the	the	DET
ajst-20808	58	20	sum	sum	NOUN
ajst-20808	58	21	of	of	ADP
ajst-20808	58	22	rewards	reward	NOUN
ajst-20808	58	23	obtained	obtain	VERB
ajst-20808	58	24	by	by	ADP
ajst-20808	58	25	the	the	DET
ajst-20808	58	26	intelligent	intelligent	ADJ
ajst-20808	58	27	body	body	NOUN
ajst-20808	58	28	during	during	ADP
ajst-20808	58	29	the	the	DET
ajst-20808	58	30	execution	execution	NOUN
ajst-20808	58	31	of	of	ADP
ajst-20808	58	32	the	the	DET
ajst-20808	58	33	task	task	NOUN
ajst-20808	58	34	.	.	PUNCT
ajst-20808	59	1	a	a	DET
ajst-20808	59	2	higher	high	ADJ
ajst-20808	59	3	cumulative	cumulative	ADJ
ajst-20808	59	4	reward	reward	NOUN
ajst-20808	59	5	means	mean	VERB
ajst-20808	59	6	that	that	SCONJ
ajst-20808	59	7	the	the	DET
ajst-20808	59	8	intelligent	intelligent	ADJ
ajst-20808	59	9	body	body	NOUN
ajst-20808	59	10	can	can	AUX
ajst-20808	59	11	perform	perform	VERB
ajst-20808	59	12	the	the	DET
ajst-20808	59	13	task	task	NOUN
ajst-20808	59	14	effectively	effectively	ADV
ajst-20808	59	15	and	and	CCONJ
ajst-20808	59	16	make	make	VERB
ajst-20808	59	17	appropriate	appropriate	ADJ
ajst-20808	59	18	decisions	decision	NOUN
ajst-20808	59	19	based	base	VERB
ajst-20808	59	20	on	on	ADP
ajst-20808	59	21	the	the	DET
ajst-20808	59	22	rewards	reward	NOUN
ajst-20808	59	23	provided	provide	VERB
ajst-20808	59	24	by	by	ADP
ajst-20808	59	25	the	the	DET
ajst-20808	59	26	environment	environment	NOUN
ajst-20808	59	27	.	.	PUNCT
ajst-20808	60	1	by	by	ADP
ajst-20808	60	2	comparing	compare	VERB
ajst-20808	60	3	the	the	DET
ajst-20808	60	4	cumulative	cumulative	ADJ
ajst-20808	60	5	rewards	reward	NOUN
ajst-20808	60	6	of	of	ADP
ajst-20808	60	7	different	different	ADJ
ajst-20808	60	8	algorithms	algorithm	NOUN
ajst-20808	60	9	on	on	ADP
ajst-20808	60	10	the	the	DET
ajst-20808	60	11	same	same	ADJ
ajst-20808	60	12	task	task	NOUN
ajst-20808	60	13	,	,	PUNCT
ajst-20808	60	14	it	it	PRON
ajst-20808	60	15	is	be	AUX
ajst-20808	60	16	possible	possible	ADJ
ajst-20808	60	17	to	to	PART
ajst-20808	60	18	determine	determine	VERB
ajst-20808	60	19	which	which	DET
ajst-20808	60	20	algorithm	algorithm	NOUN
ajst-20808	60	21	is	be	AUX
ajst-20808	60	22	better	well	ADV
ajst-20808	60	23	suited	suited	ADJ
ajst-20808	60	24	to	to	PART
ajst-20808	60	25	solve	solve	VERB
ajst-20808	60	26	a	a	DET
ajst-20808	60	27	particular	particular	ADJ
ajst-20808	60	28	problem	problem	NOUN
ajst-20808	60	29	.	.	PUNCT
ajst-20808	61	1	3.1.2	3.1.2	X
ajst-20808	61	2	.	.	PUNCT
ajst-20808	62	1	speed	speed	NOUN
ajst-20808	62	2	of	of	ADP
ajst-20808	62	3	convergence	convergence	NOUN
ajst-20808	62	4	the	the	DET
ajst-20808	62	5	convergence	convergence	NOUN
ajst-20808	62	6	speed	speed	VERB
ajst-20808	62	7	metric	metric	ADJ
ajst-20808	62	8	measures	measure	NOUN
ajst-20808	62	9	how	how	SCONJ
ajst-20808	62	10	quickly	quickly	ADV
ajst-20808	62	11	the	the	DET
ajst-20808	62	12	reinforcement	reinforcement	NOUN
ajst-20808	62	13	learning	learning	NOUN
ajst-20808	62	14	algorithm	algorithm	NOUN
ajst-20808	62	15	learns	learn	VERB
ajst-20808	62	16	the	the	DET
ajst-20808	62	17	optimal	optimal	ADJ
ajst-20808	62	18	policy	policy	NOUN
ajst-20808	62	19	.	.	PUNCT
ajst-20808	63	1	a	a	DET
ajst-20808	63	2	faster	fast	ADJ
ajst-20808	63	3	convergence	convergence	NOUN
ajst-20808	63	4	speed	speed	NOUN
ajst-20808	63	5	means	mean	VERB
ajst-20808	63	6	that	that	SCONJ
ajst-20808	63	7	the	the	DET
ajst-20808	63	8	algorithm	algorithm	NOUN
ajst-20808	63	9	can	can	AUX
ajst-20808	63	10	quickly	quickly	ADV
ajst-20808	63	11	converge	converge	VERB
ajst-20808	63	12	to	to	ADP
ajst-20808	63	13	the	the	DET
ajst-20808	63	14	optimal	optimal	ADJ
ajst-20808	63	15	policy	policy	NOUN
ajst-20808	63	16	or	or	CCONJ
ajst-20808	63	17	reach	reach	VERB
ajst-20808	63	18	a	a	DET
ajst-20808	63	19	steady	steady	ADJ
ajst-20808	63	20	state	state	NOUN
ajst-20808	63	21	,	,	PUNCT
ajst-20808	63	22	thus	thus	ADV
ajst-20808	63	23	achieving	achieve	VERB
ajst-20808	63	24	better	well	ADJ
ajst-20808	63	25	performance	performance	NOUN
ajst-20808	63	26	in	in	ADP
ajst-20808	63	27	the	the	DET
ajst-20808	63	28	same	same	ADJ
ajst-20808	63	29	training	training	NOUN
ajst-20808	63	30	time	time	NOUN
ajst-20808	63	31	.	.	PUNCT
ajst-20808	64	1	by	by	ADP
ajst-20808	64	2	comparing	compare	VERB
ajst-20808	64	3	the	the	DET
ajst-20808	64	4	convergence	convergence	NOUN
ajst-20808	64	5	speeds	speed	NOUN
ajst-20808	64	6	of	of	ADP
ajst-20808	64	7	different	different	ADJ
ajst-20808	64	8	algorithms	algorithm	NOUN
ajst-20808	64	9	with	with	ADP
ajst-20808	64	10	the	the	DET
ajst-20808	64	11	same	same	ADJ
ajst-20808	64	12	number	number	NOUN
ajst-20808	64	13	of	of	ADP
ajst-20808	64	14	training	training	NOUN
ajst-20808	64	15	rounds	round	NOUN
ajst-20808	64	16	,	,	PUNCT
ajst-20808	64	17	it	it	PRON
ajst-20808	64	18	is	be	AUX
ajst-20808	64	19	possible	possible	ADJ
ajst-20808	64	20	to	to	PART
ajst-20808	64	21	determine	determine	VERB
ajst-20808	64	22	which	which	DET
ajst-20808	64	23	algorithm	algorithm	NOUN
ajst-20808	64	24	is	be	AUX
ajst-20808	64	25	more	more	ADV
ajst-20808	64	26	efficient	efficient	ADJ
ajst-20808	64	27	.	.	PUNCT
ajst-20808	65	1	3.1.3	3.1.3	X
ajst-20808	65	2	.	.	PUNCT
ajst-20808	65	3	algorithm	algorithm	PROPN
ajst-20808	65	4	stability	stability	NOUN
ajst-20808	65	5	algorithm	algorithm	NOUN
ajst-20808	65	6	stability	stability	NOUN
ajst-20808	65	7	evaluates	evaluate	VERB
ajst-20808	65	8	the	the	DET
ajst-20808	65	9	consistency	consistency	NOUN
ajst-20808	65	10	of	of	ADP
ajst-20808	65	11	a	a	DET
ajst-20808	65	12	reinforcement	reinforcement	NOUN
ajst-20808	65	13	learning	learning	NOUN
ajst-20808	65	14	algorithm	algorithm	NOUN
ajst-20808	65	15	's	's	PART
ajst-20808	65	16	performance	performance	NOUN
ajst-20808	65	17	under	under	ADP
ajst-20808	65	18	different	different	ADJ
ajst-20808	65	19	conditions	condition	NOUN
ajst-20808	65	20	.	.	PUNCT
ajst-20808	66	1	a	a	DET
ajst-20808	66	2	stable	stable	ADJ
ajst-20808	66	3	algorithm	algorithm	NOUN
ajst-20808	66	4	should	should	AUX
ajst-20808	66	5	be	be	AUX
ajst-20808	66	6	able	able	ADJ
ajst-20808	66	7	to	to	PART
ajst-20808	66	8	produce	produce	VERB
ajst-20808	66	9	consistent	consistent	ADJ
ajst-20808	66	10	results	result	NOUN
ajst-20808	66	11	under	under	ADP
ajst-20808	66	12	different	different	ADJ
ajst-20808	66	13	environments	environment	NOUN
ajst-20808	66	14	and	and	CCONJ
ajst-20808	66	15	parameter	parameter	NOUN
ajst-20808	66	16	settings	setting	NOUN
ajst-20808	66	17	without	without	ADP
ajst-20808	66	18	being	be	AUX
ajst-20808	66	19	susceptible	susceptible	ADJ
ajst-20808	66	20	to	to	ADP
ajst-20808	66	21	noise	noise	NOUN
ajst-20808	66	22	or	or	CCONJ
ajst-20808	66	23	randomness	randomness	NOUN
ajst-20808	66	24	.	.	PUNCT
ajst-20808	67	1	by	by	ADP
ajst-20808	67	2	evaluating	evaluate	VERB
ajst-20808	67	3	the	the	DET
ajst-20808	67	4	stability	stability	NOUN
ajst-20808	67	5	of	of	ADP
ajst-20808	67	6	an	an	DET
ajst-20808	67	7	algorithm	algorithm	NOUN
ajst-20808	67	8	,	,	PUNCT
ajst-20808	67	9	the	the	DET
ajst-20808	67	10	reliability	reliability	NOUN
ajst-20808	67	11	and	and	CCONJ
ajst-20808	67	12	robustness	robustness	NOUN
ajst-20808	67	13	of	of	ADP
ajst-20808	67	14	the	the	DET
ajst-20808	67	15	algorithm	algorithm	NOUN
ajst-20808	67	16	can	can	AUX
ajst-20808	67	17	be	be	AUX
ajst-20808	67	18	determined	determine	VERB
ajst-20808	67	19	,	,	PUNCT
ajst-20808	67	20	as	as	ADV
ajst-20808	67	21	well	well	ADV
ajst-20808	67	22	as	as	ADP
ajst-20808	67	23	its	its	PRON
ajst-20808	67	24	applicability	applicability	NOUN
ajst-20808	67	25	in	in	ADP
ajst-20808	67	26	real	real	ADJ
ajst-20808	67	27	-	-	PUNCT
ajst-20808	67	28	world	world	NOUN
ajst-20808	67	29	environments	environment	NOUN
ajst-20808	67	30	.	.	PUNCT
ajst-20808	68	1	169	169	NUM
ajst-20808	68	2	3.2	3.2	NUM
ajst-20808	68	3	.	.	PUNCT
ajst-20808	69	1	training	training	NOUN
ajst-20808	69	2	and	and	CCONJ
ajst-20808	69	3	testing	testing	NOUN
ajst-20808	69	4	environment	environment	NOUN
ajst-20808	69	5	when	when	SCONJ
ajst-20808	69	6	evaluating	evaluate	VERB
ajst-20808	69	7	reinforcement	reinforcement	NOUN
ajst-20808	69	8	learning	learning	NOUN
ajst-20808	69	9	methods	method	NOUN
ajst-20808	69	10	,	,	PUNCT
ajst-20808	69	11	it	it	PRON
ajst-20808	69	12	is	be	AUX
ajst-20808	69	13	critical	critical	ADJ
ajst-20808	69	14	to	to	PART
ajst-20808	69	15	select	select	VERB
ajst-20808	69	16	appropriate	appropriate	ADJ
ajst-20808	69	17	training	training	NOUN
ajst-20808	69	18	and	and	CCONJ
ajst-20808	69	19	testing	testing	NOUN
ajst-20808	69	20	environments	environment	NOUN
ajst-20808	69	21	.	.	PUNCT
ajst-20808	70	1	these	these	DET
ajst-20808	70	2	environments	environment	NOUN
ajst-20808	70	3	should	should	AUX
ajst-20808	70	4	be	be	AUX
ajst-20808	70	5	able	able	ADJ
ajst-20808	70	6	to	to	PART
ajst-20808	70	7	adequately	adequately	ADV
ajst-20808	70	8	reflect	reflect	VERB
ajst-20808	70	9	the	the	DET
ajst-20808	70	10	characteristics	characteristic	NOUN
ajst-20808	70	11	of	of	ADP
ajst-20808	70	12	the	the	DET
ajst-20808	70	13	problem	problem	NOUN
ajst-20808	70	14	to	to	PART
ajst-20808	70	15	be	be	AUX
ajst-20808	70	16	solved	solve	VERB
ajst-20808	70	17	and	and	CCONJ
ajst-20808	70	18	provide	provide	VERB
ajst-20808	70	19	suitable	suitable	ADJ
ajst-20808	70	20	challenges	challenge	NOUN
ajst-20808	70	21	as	as	ADV
ajst-20808	70	22	well	well	ADV
ajst-20808	70	23	as	as	ADP
ajst-20808	70	24	effective	effective	ADJ
ajst-20808	70	25	performance	performance	NOUN
ajst-20808	70	26	evaluation	evaluation	NOUN
ajst-20808	70	27	.	.	PUNCT
ajst-20808	71	1	the	the	DET
ajst-20808	71	2	training	training	NOUN
ajst-20808	71	3	environment	environment	NOUN
ajst-20808	71	4	is	be	AUX
ajst-20808	71	5	the	the	DET
ajst-20808	71	6	environment	environment	NOUN
ajst-20808	71	7	with	with	ADP
ajst-20808	71	8	which	which	PRON
ajst-20808	71	9	the	the	DET
ajst-20808	71	10	intelligence	intelligence	NOUN
ajst-20808	71	11	interacts	interact	VERB
ajst-20808	71	12	,	,	PUNCT
ajst-20808	71	13	learns	learn	VERB
ajst-20808	71	14	and	and	CCONJ
ajst-20808	71	15	optimizes	optimize	VERB
ajst-20808	71	16	.	.	PUNCT
ajst-20808	72	1	this	this	DET
ajst-20808	72	2	environment	environment	NOUN
ajst-20808	72	3	should	should	AUX
ajst-20808	72	4	simulate	simulate	VERB
ajst-20808	72	5	the	the	DET
ajst-20808	72	6	key	key	ADJ
ajst-20808	72	7	features	feature	NOUN
ajst-20808	72	8	of	of	ADP
ajst-20808	72	9	the	the	DET
ajst-20808	72	10	problem	problem	NOUN
ajst-20808	72	11	to	to	PART
ajst-20808	72	12	be	be	AUX
ajst-20808	72	13	solved	solve	VERB
ajst-20808	72	14	and	and	CCONJ
ajst-20808	72	15	provide	provide	VERB
ajst-20808	72	16	enough	enough	ADJ
ajst-20808	72	17	information	information	NOUN
ajst-20808	72	18	and	and	CCONJ
ajst-20808	72	19	feedback	feedback	NOUN
ajst-20808	72	20	so	so	SCONJ
ajst-20808	72	21	that	that	SCONJ
ajst-20808	72	22	the	the	DET
ajst-20808	72	23	intelligences	intelligence	NOUN
ajst-20808	72	24	can	can	AUX
ajst-20808	72	25	gradually	gradually	ADV
ajst-20808	72	26	improve	improve	VERB
ajst-20808	72	27	their	their	PRON
ajst-20808	72	28	strategies	strategy	NOUN
ajst-20808	72	29	and	and	CCONJ
ajst-20808	72	30	learn	learn	VERB
ajst-20808	72	31	the	the	DET
ajst-20808	72	32	optimal	optimal	ADJ
ajst-20808	72	33	behavior	behavior	NOUN
ajst-20808	72	34	.	.	PUNCT
ajst-20808	73	1	the	the	DET
ajst-20808	73	2	training	training	NOUN
ajst-20808	73	3	environment	environment	NOUN
ajst-20808	73	4	should	should	AUX
ajst-20808	73	5	be	be	AUX
ajst-20808	73	6	well	well	ADV
ajst-20808	73	7	controlled	control	VERB
ajst-20808	73	8	and	and	CCONJ
ajst-20808	73	9	reproducible	reproducible	VERB
ajst-20808	73	10	so	so	SCONJ
ajst-20808	73	11	that	that	SCONJ
ajst-20808	73	12	researchers	researcher	NOUN
ajst-20808	73	13	can	can	AUX
ajst-20808	73	14	experiment	experiment	VERB
ajst-20808	73	15	and	and	CCONJ
ajst-20808	73	16	compare	compare	VERB
ajst-20808	73	17	under	under	ADP
ajst-20808	73	18	different	different	ADJ
ajst-20808	73	19	conditions	condition	NOUN
ajst-20808	73	20	.	.	PUNCT
ajst-20808	74	1	the	the	DET
ajst-20808	74	2	testing	testing	NOUN
ajst-20808	74	3	environment	environment	NOUN
ajst-20808	74	4	is	be	AUX
ajst-20808	74	5	used	use	VERB
ajst-20808	74	6	to	to	PART
ajst-20808	74	7	evaluate	evaluate	VERB
ajst-20808	74	8	the	the	DET
ajst-20808	74	9	performance	performance	NOUN
ajst-20808	74	10	and	and	CCONJ
ajst-20808	74	11	generalization	generalization	NOUN
ajst-20808	74	12	ability	ability	NOUN
ajst-20808	74	13	of	of	ADP
ajst-20808	74	14	the	the	DET
ajst-20808	74	15	trained	train	VERB
ajst-20808	74	16	intelligences	intelligence	NOUN
ajst-20808	74	17	.	.	PUNCT
ajst-20808	75	1	this	this	DET
ajst-20808	75	2	environment	environment	NOUN
ajst-20808	75	3	is	be	AUX
ajst-20808	75	4	usually	usually	ADV
ajst-20808	75	5	different	different	ADJ
ajst-20808	75	6	from	from	ADP
ajst-20808	75	7	the	the	DET
ajst-20808	75	8	training	training	NOUN
ajst-20808	75	9	environment	environment	NOUN
ajst-20808	75	10	to	to	PART
ajst-20808	75	11	ensure	ensure	VERB
ajst-20808	75	12	that	that	SCONJ
ajst-20808	75	13	the	the	DET
ajst-20808	75	14	intelligences	intelligence	NOUN
ajst-20808	75	15	are	be	AUX
ajst-20808	75	16	able	able	ADJ
ajst-20808	75	17	to	to	PART
ajst-20808	75	18	make	make	VERB
ajst-20808	75	19	accurate	accurate	ADJ
ajst-20808	75	20	decisions	decision	NOUN
ajst-20808	75	21	in	in	ADP
ajst-20808	75	22	unseen	unseen	ADJ
ajst-20808	75	23	situations	situation	NOUN
ajst-20808	75	24	.	.	PUNCT
ajst-20808	76	1	the	the	DET
ajst-20808	76	2	test	test	NOUN
ajst-20808	76	3	environment	environment	NOUN
ajst-20808	76	4	should	should	AUX
ajst-20808	76	5	contain	contain	VERB
ajst-20808	76	6	a	a	DET
ajst-20808	76	7	variety	variety	NOUN
ajst-20808	76	8	of	of	ADP
ajst-20808	76	9	possible	possible	ADJ
ajst-20808	76	10	scenarios	scenario	NOUN
ajst-20808	76	11	and	and	CCONJ
ajst-20808	76	12	challenges	challenge	NOUN
ajst-20808	76	13	to	to	PART
ajst-20808	76	14	fully	fully	ADV
ajst-20808	76	15	evaluate	evaluate	VERB
ajst-20808	76	16	the	the	DET
ajst-20808	76	17	performance	performance	NOUN
ajst-20808	76	18	of	of	ADP
ajst-20808	76	19	the	the	DET
ajst-20808	76	20	intelligences	intelligence	NOUN
ajst-20808	76	21	.	.	PUNCT
ajst-20808	77	1	at	at	ADP
ajst-20808	77	2	the	the	DET
ajst-20808	77	3	same	same	ADJ
ajst-20808	77	4	time	time	NOUN
ajst-20808	77	5	,	,	PUNCT
ajst-20808	77	6	the	the	DET
ajst-20808	77	7	test	test	NOUN
ajst-20808	77	8	environment	environment	NOUN
ajst-20808	77	9	should	should	AUX
ajst-20808	77	10	be	be	AUX
ajst-20808	77	11	set	set	VERB
ajst-20808	77	12	up	up	ADP
ajst-20808	77	13	to	to	PART
ajst-20808	77	14	match	match	VERB
ajst-20808	77	15	the	the	DET
ajst-20808	77	16	real	real	ADJ
ajst-20808	77	17	-	-	PUNCT
ajst-20808	77	18	world	world	NOUN
ajst-20808	77	19	application	application	NOUN
ajst-20808	77	20	scenarios	scenario	NOUN
ajst-20808	77	21	to	to	PART
ajst-20808	77	22	ensure	ensure	VERB
ajst-20808	77	23	the	the	DET
ajst-20808	77	24	usefulness	usefulness	NOUN
ajst-20808	77	25	and	and	CCONJ
ajst-20808	77	26	reliability	reliability	NOUN
ajst-20808	77	27	of	of	ADP
ajst-20808	77	28	the	the	DET
ajst-20808	77	29	algorithms	algorithm	NOUN
ajst-20808	77	30	.	.	PUNCT
ajst-20808	78	1	when	when	SCONJ
ajst-20808	78	2	designing	design	VERB
ajst-20808	78	3	training	training	NOUN
ajst-20808	78	4	and	and	CCONJ
ajst-20808	78	5	testing	testing	NOUN
ajst-20808	78	6	environments	environment	NOUN
ajst-20808	78	7	,	,	PUNCT
ajst-20808	78	8	there	there	PRON
ajst-20808	78	9	is	be	VERB
ajst-20808	78	10	a	a	DET
ajst-20808	78	11	need	need	NOUN
ajst-20808	78	12	to	to	PART
ajst-20808	78	13	weigh	weigh	VERB
ajst-20808	78	14	the	the	DET
ajst-20808	78	15	degree	degree	NOUN
ajst-20808	78	16	of	of	ADP
ajst-20808	78	17	interface	interface	NOUN
ajst-20808	78	18	between	between	ADP
ajst-20808	78	19	the	the	DET
ajst-20808	78	20	simulation	simulation	NOUN
ajst-20808	78	21	environment	environment	NOUN
ajst-20808	78	22	and	and	CCONJ
ajst-20808	78	23	the	the	DET
ajst-20808	78	24	real	real	ADJ
ajst-20808	78	25	world	world	NOUN
ajst-20808	78	26	.	.	PUNCT
ajst-20808	79	1	although	although	SCONJ
ajst-20808	79	2	simulation	simulation	NOUN
ajst-20808	79	3	environments	environment	NOUN
ajst-20808	79	4	can	can	AUX
ajst-20808	79	5	provide	provide	VERB
ajst-20808	79	6	better	well	ADJ
ajst-20808	79	7	controllability	controllability	NOUN
ajst-20808	79	8	and	and	CCONJ
ajst-20808	79	9	debugging	debugging	NOUN
ajst-20808	79	10	,	,	PUNCT
ajst-20808	79	11	they	they	PRON
ajst-20808	79	12	may	may	AUX
ajst-20808	79	13	not	not	PART
ajst-20808	79	14	fully	fully	ADV
ajst-20808	79	15	reflect	reflect	VERB
ajst-20808	79	16	the	the	DET
ajst-20808	79	17	complexity	complexity	NOUN
ajst-20808	79	18	and	and	CCONJ
ajst-20808	79	19	uncertainty	uncertainty	NOUN
ajst-20808	79	20	of	of	ADP
ajst-20808	79	21	the	the	DET
ajst-20808	79	22	real	real	ADJ
ajst-20808	79	23	world	world	NOUN
ajst-20808	79	24	.	.	PUNCT
ajst-20808	80	1	therefore	therefore	ADV
ajst-20808	80	2	,	,	PUNCT
ajst-20808	80	3	it	it	PRON
ajst-20808	80	4	is	be	AUX
ajst-20808	80	5	necessary	necessary	ADJ
ajst-20808	80	6	to	to	PART
ajst-20808	80	7	consider	consider	VERB
ajst-20808	80	8	the	the	DET
ajst-20808	80	9	possibility	possibility	NOUN
ajst-20808	80	10	of	of	ADP
ajst-20808	80	11	validating	validate	VERB
ajst-20808	80	12	and	and	CCONJ
ajst-20808	80	13	tuning	tune	VERB
ajst-20808	80	14	algorithms	algorithm	NOUN
ajst-20808	80	15	in	in	ADP
ajst-20808	80	16	the	the	DET
ajst-20808	80	17	real	real	ADJ
ajst-20808	80	18	world	world	NOUN
ajst-20808	80	19	during	during	ADP
ajst-20808	80	20	the	the	DET
ajst-20808	80	21	testing	testing	NOUN
ajst-20808	80	22	phase	phase	NOUN
ajst-20808	80	23	to	to	PART
ajst-20808	80	24	ensure	ensure	VERB
ajst-20808	80	25	their	their	PRON
ajst-20808	80	26	reliability	reliability	NOUN
ajst-20808	80	27	and	and	CCONJ
ajst-20808	80	28	effectiveness	effectiveness	NOUN
ajst-20808	80	29	in	in	ADP
ajst-20808	80	30	real	real	ADJ
ajst-20808	80	31	-	-	PUNCT
ajst-20808	80	32	world	world	NOUN
ajst-20808	80	33	applications	application	NOUN
ajst-20808	80	34	.	.	PUNCT
ajst-20808	81	1	the	the	DET
ajst-20808	81	2	selection	selection	NOUN
ajst-20808	81	3	of	of	ADP
ajst-20808	81	4	suitable	suitable	ADJ
ajst-20808	81	5	training	training	NOUN
ajst-20808	81	6	and	and	CCONJ
ajst-20808	81	7	testing	testing	NOUN
ajst-20808	81	8	environments	environment	NOUN
ajst-20808	81	9	is	be	AUX
ajst-20808	81	10	crucial	crucial	ADJ
ajst-20808	81	11	for	for	ADP
ajst-20808	81	12	evaluating	evaluate	VERB
ajst-20808	81	13	the	the	DET
ajst-20808	81	14	performance	performance	NOUN
ajst-20808	81	15	of	of	ADP
ajst-20808	81	16	reinforcement	reinforcement	NOUN
ajst-20808	81	17	learning	learning	NOUN
ajst-20808	81	18	methods	method	NOUN
ajst-20808	81	19	.	.	PUNCT
ajst-20808	82	1	by	by	ADP
ajst-20808	82	2	designing	design	VERB
ajst-20808	82	3	suitable	suitable	ADJ
ajst-20808	82	4	environments	environment	NOUN
ajst-20808	82	5	and	and	CCONJ
ajst-20808	82	6	rationally	rationally	ADV
ajst-20808	82	7	utilizing	utilize	VERB
ajst-20808	82	8	training	training	NOUN
ajst-20808	82	9	and	and	CCONJ
ajst-20808	82	10	testing	testing	NOUN
ajst-20808	82	11	data	datum	NOUN
ajst-20808	82	12	during	during	ADP
ajst-20808	82	13	experiments	experiment	NOUN
ajst-20808	82	14	,	,	PUNCT
ajst-20808	82	15	the	the	DET
ajst-20808	82	16	effects	effect	NOUN
ajst-20808	82	17	of	of	ADP
ajst-20808	82	18	algorithms	algorithm	NOUN
ajst-20808	82	19	can	can	AUX
ajst-20808	82	20	be	be	AUX
ajst-20808	82	21	more	more	ADV
ajst-20808	82	22	accurately	accurately	ADV
ajst-20808	82	23	assessed	assess	VERB
ajst-20808	82	24	and	and	CCONJ
ajst-20808	82	25	their	their	PRON
ajst-20808	82	26	feasibility	feasibility	NOUN
ajst-20808	82	27	and	and	CCONJ
ajst-20808	82	28	reliability	reliability	NOUN
ajst-20808	82	29	in	in	ADP
ajst-20808	82	30	real	real	ADJ
ajst-20808	82	31	-	-	PUNCT
ajst-20808	82	32	world	world	NOUN
ajst-20808	82	33	applications	application	NOUN
ajst-20808	82	34	can	can	AUX
ajst-20808	82	35	be	be	AUX
ajst-20808	82	36	improved	improve	VERB
ajst-20808	82	37	.	.	PUNCT
ajst-20808	83	1	3.3	3.3	NUM
ajst-20808	83	2	.	.	PUNCT
ajst-20808	83	3	algorithm	algorithm	NOUN
ajst-20808	83	4	complexity	complexity	NOUN
ajst-20808	83	5	and	and	CCONJ
ajst-20808	83	6	scalability	scalability	NOUN
ajst-20808	83	7	evaluating	evaluate	VERB
ajst-20808	83	8	the	the	DET
ajst-20808	83	9	algorithmic	algorithmic	ADJ
ajst-20808	83	10	complexity	complexity	NOUN
ajst-20808	83	11	and	and	CCONJ
ajst-20808	83	12	scalability	scalability	NOUN
ajst-20808	83	13	of	of	ADP
ajst-20808	83	14	reinforcement	reinforcement	NOUN
ajst-20808	83	15	learning	learning	NOUN
ajst-20808	83	16	methods	method	NOUN
ajst-20808	83	17	is	be	AUX
ajst-20808	83	18	an	an	DET
ajst-20808	83	19	important	important	ADJ
ajst-20808	83	20	consideration	consideration	NOUN
ajst-20808	83	21	to	to	PART
ajst-20808	83	22	ensure	ensure	VERB
ajst-20808	83	23	the	the	DET
ajst-20808	83	24	feasibility	feasibility	NOUN
ajst-20808	83	25	and	and	CCONJ
ajst-20808	83	26	efficiency	efficiency	NOUN
ajst-20808	83	27	of	of	ADP
ajst-20808	83	28	the	the	DET
ajst-20808	83	29	algorithms	algorithm	NOUN
ajst-20808	83	30	in	in	ADP
ajst-20808	83	31	practical	practical	ADJ
ajst-20808	83	32	applications	application	NOUN
ajst-20808	83	33	.	.	PUNCT
ajst-20808	84	1	the	the	DET
ajst-20808	84	2	algorithmic	algorithmic	ADJ
ajst-20808	84	3	complexity	complexity	NOUN
ajst-20808	84	4	metric	metric	ADJ
ajst-20808	84	5	assesses	assesse	NOUN
ajst-20808	84	6	how	how	SCONJ
ajst-20808	84	7	much	much	ADJ
ajst-20808	84	8	the	the	DET
ajst-20808	84	9	reinforcement	reinforcement	NOUN
ajst-20808	84	10	learning	learn	VERB
ajst-20808	84	11	algorithm	algorithm	NOUN
ajst-20808	84	12	consumes	consume	NOUN
ajst-20808	84	13	in	in	ADP
ajst-20808	84	14	terms	term	NOUN
ajst-20808	84	15	of	of	ADP
ajst-20808	84	16	computational	computational	ADJ
ajst-20808	84	17	and	and	CCONJ
ajst-20808	84	18	memory	memory	NOUN
ajst-20808	84	19	resources	resource	NOUN
ajst-20808	84	20	.	.	PUNCT
ajst-20808	85	1	this	this	PRON
ajst-20808	85	2	includes	include	VERB
ajst-20808	85	3	the	the	DET
ajst-20808	85	4	algorithm	algorithm	NOUN
ajst-20808	85	5	's	's	PART
ajst-20808	85	6	time	time	NOUN
ajst-20808	85	7	complexity	complexity	NOUN
ajst-20808	85	8	,	,	PUNCT
ajst-20808	85	9	space	space	NOUN
ajst-20808	85	10	complexity	complexity	NOUN
ajst-20808	85	11	,	,	PUNCT
ajst-20808	85	12	and	and	CCONJ
ajst-20808	85	13	computational	computational	ADJ
ajst-20808	85	14	resource	resource	NOUN
ajst-20808	85	15	requirements	requirement	NOUN
ajst-20808	85	16	.	.	PUNCT
ajst-20808	86	1	algorithms	algorithm	NOUN
ajst-20808	86	2	with	with	ADP
ajst-20808	86	3	low	low	ADJ
ajst-20808	86	4	algorithmic	algorithmic	ADJ
ajst-20808	86	5	complexity	complexity	NOUN
ajst-20808	86	6	are	be	AUX
ajst-20808	86	7	able	able	ADJ
ajst-20808	86	8	to	to	PART
ajst-20808	86	9	execute	execute	VERB
ajst-20808	86	10	efficiently	efficiently	ADV
ajst-20808	86	11	with	with	ADP
ajst-20808	86	12	limited	limited	ADJ
ajst-20808	86	13	computational	computational	ADJ
ajst-20808	86	14	resources	resource	NOUN
ajst-20808	86	15	,	,	PUNCT
ajst-20808	86	16	making	make	VERB
ajst-20808	86	17	them	they	PRON
ajst-20808	86	18	more	more	ADV
ajst-20808	86	19	suitable	suitable	ADJ
ajst-20808	86	20	for	for	ADP
ajst-20808	86	21	deployment	deployment	NOUN
ajst-20808	86	22	and	and	CCONJ
ajst-20808	86	23	operation	operation	NOUN
ajst-20808	86	24	in	in	ADP
ajst-20808	86	25	real	real	ADJ
ajst-20808	86	26	-	-	PUNCT
ajst-20808	86	27	world	world	NOUN
ajst-20808	86	28	applications	application	NOUN
ajst-20808	86	29	.	.	PUNCT
ajst-20808	87	1	the	the	DET
ajst-20808	87	2	scalability	scalability	NOUN
ajst-20808	87	3	metric	metric	NOUN
ajst-20808	87	4	evaluates	evaluate	VERB
ajst-20808	87	5	the	the	DET
ajst-20808	87	6	ability	ability	NOUN
ajst-20808	87	7	of	of	ADP
ajst-20808	87	8	a	a	DET
ajst-20808	87	9	reinforcement	reinforcement	NOUN
ajst-20808	87	10	learning	learn	VERB
ajst-20808	87	11	algorithm	algorithm	NOUN
ajst-20808	87	12	to	to	PART
ajst-20808	87	13	handle	handle	VERB
ajst-20808	87	14	largescale	largescale	ADJ
ajst-20808	87	15	problems	problem	NOUN
ajst-20808	87	16	.	.	PUNCT
ajst-20808	88	1	an	an	DET
ajst-20808	88	2	algorithm	algorithm	NOUN
ajst-20808	88	3	with	with	ADP
ajst-20808	88	4	good	good	ADJ
ajst-20808	88	5	scalability	scalability	NOUN
ajst-20808	88	6	can	can	AUX
ajst-20808	88	7	efficiently	efficiently	ADV
ajst-20808	88	8	handle	handle	VERB
ajst-20808	88	9	a	a	DET
ajst-20808	88	10	large	large	ADJ
ajst-20808	88	11	number	number	NOUN
ajst-20808	88	12	of	of	ADP
ajst-20808	88	13	state	state	NOUN
ajst-20808	88	14	spaces	space	NOUN
ajst-20808	88	15	and	and	CCONJ
ajst-20808	88	16	action	action	NOUN
ajst-20808	88	17	spaces	space	NOUN
ajst-20808	88	18	without	without	ADP
ajst-20808	88	19	suffering	suffer	VERB
ajst-20808	88	20	from	from	ADP
ajst-20808	88	21	performance	performance	NOUN
ajst-20808	88	22	degradation	degradation	NOUN
ajst-20808	88	23	or	or	CCONJ
ajst-20808	88	24	resource	resource	NOUN
ajst-20808	88	25	exhaustion	exhaustion	NOUN
ajst-20808	88	26	.	.	PUNCT
ajst-20808	89	1	when	when	SCONJ
ajst-20808	89	2	evaluating	evaluate	VERB
ajst-20808	89	3	scalability	scalability	NOUN
ajst-20808	89	4	,	,	PUNCT
ajst-20808	89	5	it	it	PRON
ajst-20808	89	6	is	be	AUX
ajst-20808	89	7	necessary	necessary	ADJ
ajst-20808	89	8	to	to	PART
ajst-20808	89	9	consider	consider	VERB
ajst-20808	89	10	the	the	DET
ajst-20808	89	11	performance	performance	NOUN
ajst-20808	89	12	of	of	ADP
ajst-20808	89	13	the	the	DET
ajst-20808	89	14	algorithm	algorithm	NOUN
ajst-20808	89	15	on	on	ADP
ajst-20808	89	16	problems	problem	NOUN
ajst-20808	89	17	of	of	ADP
ajst-20808	89	18	different	different	ADJ
ajst-20808	89	19	sizes	size	NOUN
ajst-20808	89	20	and	and	CCONJ
ajst-20808	89	21	determine	determine	VERB
ajst-20808	89	22	its	its	PRON
ajst-20808	89	23	applicability	applicability	NOUN
ajst-20808	89	24	and	and	CCONJ
ajst-20808	89	25	efficiency	efficiency	NOUN
ajst-20808	89	26	on	on	ADP
ajst-20808	89	27	large	large	ADJ
ajst-20808	89	28	-	-	PUNCT
ajst-20808	89	29	scale	scale	NOUN
ajst-20808	89	30	problems.the	problems.the	PRON
ajst-20808	89	31	recently	recently	ADV
ajst-20808	89	32	proposed	propose	VERB
ajst-20808	89	33	parameter	parameter	NOUN
ajst-20808	89	34	efficient	efficient	ADJ
ajst-20808	89	35	fine	fine	ADV
ajst-20808	89	36	-	-	PUNCT
ajst-20808	89	37	tuning	tuning	NOUN
ajst-20808	89	38	(	(	PUNCT
ajst-20808	89	39	peft	peft	ADJ
ajst-20808	89	40	)	)	PUNCT
ajst-20808	89	41	strategy	strategy	NOUN
ajst-20808	89	42	for	for	ADP
ajst-20808	89	43	language	language	NOUN
ajst-20808	89	44	models	model	NOUN
ajst-20808	89	45	has	have	AUX
ajst-20808	89	46	demonstrated	demonstrate	VERB
ajst-20808	89	47	lower	low	ADJ
ajst-20808	89	48	algorithmic	algorithmic	ADJ
ajst-20808	89	49	complexity	complexity	NOUN
ajst-20808	89	50	during	during	ADP
ajst-20808	89	51	implementation	implementation	NOUN
ajst-20808	89	52	,	,	PUNCT
ajst-20808	89	53	along	along	ADP
ajst-20808	89	54	with	with	ADP
ajst-20808	89	55	the	the	DET
ajst-20808	89	56	potential	potential	NOUN
ajst-20808	89	57	for	for	ADP
ajst-20808	89	58	efficient	efficient	ADJ
ajst-20808	89	59	execution	execution	NOUN
ajst-20808	89	60	under	under	ADP
ajst-20808	89	61	limited	limited	ADJ
ajst-20808	89	62	computational	computational	ADJ
ajst-20808	89	63	resources.[5	resources.[5	X
ajst-20808	89	64	]	]	PUNCT
ajst-20808	89	65	parallelism	parallelism	NOUN
ajst-20808	89	66	enables	enable	VERB
ajst-20808	89	67	the	the	DET
ajst-20808	89	68	use	use	NOUN
ajst-20808	89	69	of	of	ADP
ajst-20808	89	70	parallel	parallel	ADJ
ajst-20808	89	71	computing	computing	NOUN
ajst-20808	89	72	resources	resource	NOUN
ajst-20808	89	73	to	to	PART
ajst-20808	89	74	accelerate	accelerate	VERB
ajst-20808	89	75	the	the	DET
ajst-20808	89	76	process	process	NOUN
ajst-20808	89	77	of	of	ADP
ajst-20808	89	78	algorithm	algorithm	NOUN
ajst-20808	89	79	execution	execution	NOUN
ajst-20808	89	80	,	,	PUNCT
ajst-20808	89	81	thereby	thereby	ADV
ajst-20808	89	82	increasing	increase	VERB
ajst-20808	89	83	its	its	PRON
ajst-20808	89	84	efficiency	efficiency	NOUN
ajst-20808	89	85	and	and	CCONJ
ajst-20808	89	86	performance	performance	NOUN
ajst-20808	89	87	on	on	ADP
ajst-20808	89	88	large	large	ADJ
ajst-20808	89	89	-	-	PUNCT
ajst-20808	89	90	scale	scale	NOUN
ajst-20808	89	91	problems	problem	NOUN
ajst-20808	89	92	.	.	PUNCT
ajst-20808	90	1	the	the	DET
ajst-20808	90	2	scalability	scalability	NOUN
ajst-20808	90	3	and	and	CCONJ
ajst-20808	90	4	parallelism	parallelism	NOUN
ajst-20808	90	5	of	of	ADP
ajst-20808	90	6	algorithms	algorithm	NOUN
ajst-20808	90	7	can	can	AUX
ajst-20808	90	8	be	be	AUX
ajst-20808	90	9	effectively	effectively	ADV
ajst-20808	90	10	improved	improve	VERB
ajst-20808	90	11	by	by	ADP
ajst-20808	90	12	designing	design	VERB
ajst-20808	90	13	algorithmic	algorithmic	ADJ
ajst-20808	90	14	structures	structure	NOUN
ajst-20808	90	15	and	and	CCONJ
ajst-20808	90	16	optimizing	optimize	VERB
ajst-20808	90	17	algorithmic	algorithmic	ADJ
ajst-20808	90	18	implementations	implementation	NOUN
ajst-20808	90	19	that	that	PRON
ajst-20808	90	20	support	support	VERB
ajst-20808	90	21	parallel	parallel	ADJ
ajst-20808	90	22	computing	computing	NOUN
ajst-20808	90	23	.	.	PUNCT
ajst-20808	91	1	for	for	ADP
ajst-20808	91	2	large	large	ADJ
ajst-20808	91	3	-	-	PUNCT
ajst-20808	91	4	scale	scale	NOUN
ajst-20808	91	5	problems	problem	NOUN
ajst-20808	91	6	,	,	PUNCT
ajst-20808	91	7	distributed	distribute	VERB
ajst-20808	91	8	computing	computing	NOUN
ajst-20808	91	9	techniques	technique	NOUN
ajst-20808	91	10	can	can	AUX
ajst-20808	91	11	further	far	ADV
ajst-20808	91	12	improve	improve	VERB
ajst-20808	91	13	the	the	DET
ajst-20808	91	14	scalability	scalability	NOUN
ajst-20808	91	15	and	and	CCONJ
ajst-20808	91	16	performance	performance	NOUN
ajst-20808	91	17	of	of	ADP
ajst-20808	91	18	algorithms	algorithm	NOUN
ajst-20808	91	19	.	.	PUNCT
ajst-20808	92	1	by	by	ADP
ajst-20808	92	2	assigning	assign	VERB
ajst-20808	92	3	computational	computational	ADJ
ajst-20808	92	4	tasks	task	NOUN
ajst-20808	92	5	to	to	ADP
ajst-20808	92	6	multiple	multiple	ADJ
ajst-20808	92	7	computing	compute	VERB
ajst-20808	92	8	nodes	node	NOUN
ajst-20808	92	9	for	for	ADP
ajst-20808	92	10	parallel	parallel	ADJ
ajst-20808	92	11	processing	processing	NOUN
ajst-20808	92	12	,	,	PUNCT
ajst-20808	92	13	computational	computational	ADJ
ajst-20808	92	14	time	time	NOUN
ajst-20808	92	15	and	and	CCONJ
ajst-20808	92	16	resource	resource	NOUN
ajst-20808	92	17	consumption	consumption	NOUN
ajst-20808	92	18	can	can	AUX
ajst-20808	92	19	be	be	AUX
ajst-20808	92	20	effectively	effectively	ADV
ajst-20808	92	21	reduced	reduce	VERB
ajst-20808	92	22	.	.	PUNCT
ajst-20808	93	1	when	when	SCONJ
ajst-20808	93	2	evaluating	evaluate	VERB
ajst-20808	93	3	the	the	DET
ajst-20808	93	4	scalability	scalability	NOUN
ajst-20808	93	5	of	of	ADP
ajst-20808	93	6	an	an	DET
ajst-20808	93	7	algorithm	algorithm	NOUN
ajst-20808	93	8	,	,	PUNCT
ajst-20808	93	9	it	it	PRON
ajst-20808	93	10	is	be	AUX
ajst-20808	93	11	necessary	necessary	ADJ
ajst-20808	93	12	to	to	PART
ajst-20808	93	13	consider	consider	VERB
ajst-20808	93	14	whether	whether	SCONJ
ajst-20808	93	15	it	it	PRON
ajst-20808	93	16	supports	support	VERB
ajst-20808	93	17	distributed	distribute	VERB
ajst-20808	93	18	computing	computing	NOUN
ajst-20808	93	19	and	and	CCONJ
ajst-20808	93	20	verify	verify	VERB
ajst-20808	93	21	its	its	PRON
ajst-20808	93	22	performance	performance	NOUN
ajst-20808	93	23	in	in	ADP
ajst-20808	93	24	a	a	DET
ajst-20808	93	25	distributed	distribute	VERB
ajst-20808	93	26	environment	environment	NOUN
ajst-20808	93	27	through	through	ADP
ajst-20808	93	28	experiments	experiment	NOUN
ajst-20808	93	29	.	.	PUNCT
ajst-20808	94	1	comprehensive	comprehensive	ADJ
ajst-20808	94	2	consideration	consideration	NOUN
ajst-20808	94	3	of	of	ADP
ajst-20808	94	4	factors	factor	NOUN
ajst-20808	94	5	such	such	ADJ
ajst-20808	94	6	as	as	ADP
ajst-20808	94	7	algorithm	algorithm	NOUN
ajst-20808	94	8	complexity	complexity	NOUN
ajst-20808	94	9	and	and	CCONJ
ajst-20808	94	10	scalability	scalability	NOUN
ajst-20808	94	11	can	can	AUX
ajst-20808	94	12	help	help	VERB
ajst-20808	94	13	assess	assess	VERB
ajst-20808	94	14	the	the	DET
ajst-20808	94	15	feasibility	feasibility	NOUN
ajst-20808	94	16	and	and	CCONJ
ajst-20808	94	17	efficiency	efficiency	NOUN
ajst-20808	94	18	of	of	ADP
ajst-20808	94	19	reinforcement	reinforcement	NOUN
ajst-20808	94	20	learning	learning	NOUN
ajst-20808	94	21	methods	method	NOUN
ajst-20808	94	22	in	in	ADP
ajst-20808	94	23	practical	practical	ADJ
ajst-20808	94	24	applications	application	NOUN
ajst-20808	94	25	.	.	PUNCT
ajst-20808	95	1	by	by	ADP
ajst-20808	95	2	choosing	choose	VERB
ajst-20808	95	3	algorithms	algorithm	NOUN
ajst-20808	95	4	with	with	ADP
ajst-20808	95	5	low	low	ADJ
ajst-20808	95	6	algorithm	algorithm	NOUN
ajst-20808	95	7	complexity	complexity	NOUN
ajst-20808	95	8	and	and	CCONJ
ajst-20808	95	9	good	good	ADJ
ajst-20808	95	10	scalability	scalability	NOUN
ajst-20808	95	11	,	,	PUNCT
ajst-20808	95	12	and	and	CCONJ
ajst-20808	95	13	optimizing	optimize	VERB
ajst-20808	95	14	the	the	DET
ajst-20808	95	15	algorithm	algorithm	NOUN
ajst-20808	95	16	implementation	implementation	NOUN
ajst-20808	95	17	to	to	PART
ajst-20808	95	18	improve	improve	VERB
ajst-20808	95	19	parallelism	parallelism	NOUN
ajst-20808	95	20	and	and	CCONJ
ajst-20808	95	21	distributed	distribute	VERB
ajst-20808	95	22	computing	computing	NOUN
ajst-20808	95	23	capability	capability	NOUN
ajst-20808	95	24	,	,	PUNCT
ajst-20808	95	25	the	the	DET
ajst-20808	95	26	practicality	practicality	NOUN
ajst-20808	95	27	and	and	CCONJ
ajst-20808	95	28	performance	performance	NOUN
ajst-20808	95	29	performance	performance	NOUN
ajst-20808	95	30	of	of	ADP
ajst-20808	95	31	the	the	DET
ajst-20808	95	32	algorithms	algorithm	NOUN
ajst-20808	95	33	can	can	AUX
ajst-20808	95	34	be	be	AUX
ajst-20808	95	35	effectively	effectively	ADV
ajst-20808	95	36	improved	improve	VERB
ajst-20808	95	37	.	.	PUNCT
ajst-20808	96	1	4	4	X
ajst-20808	96	2	.	.	X
ajst-20808	96	3	reinforcement	reinforcement	NOUN
ajst-20808	96	4	learning	learning	NOUN
ajst-20808	96	5	algorithm	algorithm	PROPN
ajst-20808	96	6	performance	performance	NOUN
ajst-20808	96	7	comparison	comparison	NOUN
ajst-20808	96	8	,	,	PUNCT
ajst-20808	96	9	advantages	advantage	NOUN
ajst-20808	96	10	and	and	CCONJ
ajst-20808	96	11	disadvantages	disadvantage	VERB
ajst-20808	96	12	analysis	analysis	NOUN
ajst-20808	96	13	and	and	CCONJ
ajst-20808	96	14	its	its	PRON
ajst-20808	96	15	applicable	applicable	ADJ
ajst-20808	96	16	scenarios	scenario	NOUN
ajst-20808	96	17	4.1	4.1	NUM
ajst-20808	96	18	.	.	PUNCT
ajst-20808	97	1	algorithm	algorithm	NOUN
ajst-20808	97	2	performance	performance	NOUN
ajst-20808	97	3	comparison	comparison	NOUN
ajst-20808	97	4	both	both	DET
ajst-20808	97	5	q	q	NOUN
ajst-20808	97	6	-	-	PUNCT
ajst-20808	97	7	learning	learning	NOUN
ajst-20808	97	8	and	and	CCONJ
ajst-20808	97	9	sarsa	sarsa	NOUN
ajst-20808	97	10	are	be	AUX
ajst-20808	97	11	value	value	NOUN
ajst-20808	97	12	function	function	NOUN
ajst-20808	97	13	based	base	VERB
ajst-20808	97	14	methods	method	NOUN
ajst-20808	97	15	for	for	ADP
ajst-20808	97	16	learning	learn	VERB
ajst-20808	97	17	optimal	optimal	ADJ
ajst-20808	97	18	policies	policy	NOUN
ajst-20808	97	19	.	.	PUNCT
ajst-20808	98	1	in	in	ADP
ajst-20808	98	2	general	general	ADJ
ajst-20808	98	3	,	,	PUNCT
ajst-20808	98	4	q	q	ADJ
ajst-20808	98	5	-	-	PUNCT
ajst-20808	98	6	learning	learning	NOUN
ajst-20808	98	7	is	be	AUX
ajst-20808	98	8	more	more	ADV
ajst-20808	98	9	inclined	inclined	ADJ
ajst-20808	98	10	to	to	PART
ajst-20808	98	11	explore	explore	VERB
ajst-20808	98	12	actions	action	NOUN
ajst-20808	98	13	that	that	PRON
ajst-20808	98	14	maximize	maximize	VERB
ajst-20808	98	15	rewards	reward	NOUN
ajst-20808	98	16	during	during	ADP
ajst-20808	98	17	offline	offline	ADJ
ajst-20808	98	18	learning	learning	NOUN
ajst-20808	98	19	,	,	PUNCT
ajst-20808	98	20	while	while	SCONJ
ajst-20808	98	21	sarsa	sarsa	NOUN
ajst-20808	98	22	is	be	AUX
ajst-20808	98	23	more	more	ADV
ajst-20808	98	24	concerned	concerned	ADJ
ajst-20808	98	25	with	with	ADP
ajst-20808	98	26	the	the	DET
ajst-20808	98	27	stability	stability	NOUN
ajst-20808	98	28	of	of	ADP
ajst-20808	98	29	online	online	ADJ
ajst-20808	98	30	strategies	strategy	NOUN
ajst-20808	98	31	.	.	PUNCT
ajst-20808	99	1	sarsa	sarsa	PROPN
ajst-20808	99	2	may	may	AUX
ajst-20808	99	3	outperform	outperform	VERB
ajst-20808	99	4	qlearning	qlearne	VERB
ajst-20808	99	5	in	in	ADP
ajst-20808	99	6	terms	term	NOUN
ajst-20808	99	7	of	of	ADP
ajst-20808	99	8	stability	stability	NOUN
ajst-20808	99	9	,	,	PUNCT
ajst-20808	99	10	especially	especially	ADV
ajst-20808	99	11	in	in	ADP
ajst-20808	99	12	environments	environment	NOUN
ajst-20808	99	13	with	with	ADP
ajst-20808	99	14	a	a	DET
ajst-20808	99	15	high	high	ADJ
ajst-20808	99	16	degree	degree	NOUN
ajst-20808	99	17	of	of	ADP
ajst-20808	99	18	randomness	randomness	NOUN
ajst-20808	99	19	.	.	PUNCT
ajst-20808	100	1	both	both	DET
ajst-20808	100	2	dqn	dqn	ADJ
ajst-20808	100	3	and	and	CCONJ
ajst-20808	100	4	ddpg	ddpg	ADJ
ajst-20808	100	5	are	be	AUX
ajst-20808	100	6	deep	deep	ADJ
ajst-20808	100	7	neural	neural	ADJ
ajst-20808	100	8	network	network	NOUN
ajst-20808	100	9	-	-	PUNCT
ajst-20808	100	10	based	base	VERB
ajst-20808	100	11	approaches	approach	NOUN
ajst-20808	100	12	for	for	ADP
ajst-20808	100	13	dealing	deal	VERB
ajst-20808	100	14	with	with	ADP
ajst-20808	100	15	continuous	continuous	ADJ
ajst-20808	100	16	states	state	NOUN
ajst-20808	100	17	and	and	CCONJ
ajst-20808	100	18	action	action	NOUN
ajst-20808	100	19	spaces.dqn	spaces.dqn	NOUN
ajst-20808	100	20	is	be	AUX
ajst-20808	100	21	mainly	mainly	ADV
ajst-20808	100	22	used	use	VERB
ajst-20808	100	23	for	for	ADP
ajst-20808	100	24	problems	problem	NOUN
ajst-20808	100	25	in	in	ADP
ajst-20808	100	26	discrete	discrete	ADJ
ajst-20808	100	27	action	action	NOUN
ajst-20808	100	28	spaces	space	NOUN
ajst-20808	100	29	,	,	PUNCT
ajst-20808	100	30	while	while	SCONJ
ajst-20808	100	31	ddpg	ddpg	ADJ
ajst-20808	100	32	is	be	AUX
ajst-20808	100	33	suitable	suitable	ADJ
ajst-20808	100	34	for	for	ADP
ajst-20808	100	35	continuous	continuous	ADJ
ajst-20808	100	36	action	action	NOUN
ajst-20808	100	37	spaces	space	NOUN
ajst-20808	100	38	.	.	PUNCT
ajst-20808	101	1	ddpg	ddpg	ADJ
ajst-20808	101	2	tends	tend	VERB
ajst-20808	101	3	to	to	PART
ajst-20808	101	4	achieve	achieve	VERB
ajst-20808	101	5	better	well	ADJ
ajst-20808	101	6	performance	performance	NOUN
ajst-20808	101	7	when	when	SCONJ
ajst-20808	101	8	dealing	deal	VERB
ajst-20808	101	9	with	with	ADP
ajst-20808	101	10	continuous	continuous	ADJ
ajst-20808	101	11	action	action	NOUN
ajst-20808	101	12	space	space	NOUN
ajst-20808	101	13	.	.	PUNCT
ajst-20808	102	1	however	however	ADV
ajst-20808	102	2	,	,	PUNCT
ajst-20808	102	3	dqn	dqn	NOUN
ajst-20808	102	4	also	also	ADV
ajst-20808	102	5	performs	perform	VERB
ajst-20808	102	6	well	well	ADV
ajst-20808	102	7	on	on	ADP
ajst-20808	102	8	simple	simple	ADJ
ajst-20808	102	9	discrete	discrete	ADJ
ajst-20808	102	10	action	action	NOUN
ajst-20808	102	11	space	space	NOUN
ajst-20808	102	12	problems	problem	NOUN
ajst-20808	102	13	.	.	PUNCT
ajst-20808	103	1	reinforce	reinforce	VERB
ajst-20808	103	2	and	and	CCONJ
ajst-20808	103	3	ppo	ppo	PROPN
ajst-20808	103	4	are	be	AUX
ajst-20808	103	5	both	both	PRON
ajst-20808	103	6	policy	policy	NOUN
ajst-20808	103	7	gradient	gradient	NOUN
ajst-20808	103	8	based	base	VERB
ajst-20808	103	9	methods	method	NOUN
ajst-20808	103	10	,	,	PUNCT
ajst-20808	103	11	but	but	CCONJ
ajst-20808	103	12	differ	differ	VERB
ajst-20808	103	13	slightly	slightly	ADV
ajst-20808	103	14	in	in	ADP
ajst-20808	103	15	the	the	DET
ajst-20808	103	16	way	way	NOUN
ajst-20808	103	17	they	they	PRON
ajst-20808	103	18	update	update	VERB
ajst-20808	103	19	the	the	DET
ajst-20808	103	20	policy	policy	NOUN
ajst-20808	103	21	and	and	CCONJ
ajst-20808	103	22	their	their	PRON
ajst-20808	103	23	optimization	optimization	NOUN
ajst-20808	103	24	goals	goal	NOUN
ajst-20808	103	25	.	.	PUNCT
ajst-20808	104	1	reinforce	reinforce	NOUN
ajst-20808	104	2	is	be	AUX
ajst-20808	104	3	based	base	VERB
ajst-20808	104	4	on	on	ADP
ajst-20808	104	5	monte	monte	PROPN
ajst-20808	104	6	carlo	carlo	PROPN
ajst-20808	104	7	estimation	estimation	NOUN
ajst-20808	104	8	of	of	ADP
ajst-20808	104	9	the	the	DET
ajst-20808	104	10	samples	sample	NOUN
ajst-20808	104	11	,	,	PUNCT
ajst-20808	104	12	while	while	SCONJ
ajst-20808	104	13	ppo	ppo	PROPN
ajst-20808	104	14	uses	use	VERB
ajst-20808	104	15	proximal	proximal	ADJ
ajst-20808	104	16	policy	policy	NOUN
ajst-20808	104	17	optimization	optimization	NOUN
ajst-20808	104	18	to	to	PART
ajst-20808	104	19	ensure	ensure	VERB
ajst-20808	104	20	the	the	DET
ajst-20808	104	21	stability	stability	NOUN
ajst-20808	104	22	of	of	ADP
ajst-20808	104	23	the	the	DET
ajst-20808	104	24	updates	update	NOUN
ajst-20808	104	25	.	.	PUNCT
ajst-20808	105	1	in	in	ADP
ajst-20808	105	2	most	most	ADJ
ajst-20808	105	3	cases	case	NOUN
ajst-20808	105	4	,	,	PUNCT
ajst-20808	105	5	ppo	ppo	PROPN
ajst-20808	105	6	outperforms	outperform	NOUN
ajst-20808	105	7	reinforce	reinforce	VERB
ajst-20808	105	8	in	in	ADP
ajst-20808	105	9	terms	term	NOUN
ajst-20808	105	10	of	of	ADP
ajst-20808	105	11	performance	performance	NOUN
ajst-20808	105	12	and	and	CCONJ
ajst-20808	105	13	convergence	convergence	NOUN
ajst-20808	105	14	speed	speed	NOUN
ajst-20808	105	15	.	.	PUNCT
ajst-20808	106	1	both	both	DET
ajst-20808	106	2	trpo	trpo	NOUN
ajst-20808	106	3	and	and	CCONJ
ajst-20808	106	4	ppo	ppo	PROPN
ajst-20808	106	5	are	be	AUX
ajst-20808	106	6	policy	policy	NOUN
ajst-20808	106	7	-	-	PUNCT
ajst-20808	106	8	based	base	VERB
ajst-20808	106	9	methods	method	NOUN
ajst-20808	106	10	that	that	PRON
ajst-20808	106	11	aim	aim	VERB
ajst-20808	106	12	to	to	PART
ajst-20808	106	13	learn	learn	VERB
ajst-20808	106	14	the	the	DET
ajst-20808	106	15	optimal	optimal	ADJ
ajst-20808	106	16	policy	policy	NOUN
ajst-20808	106	17	by	by	ADP
ajst-20808	106	18	optimizing	optimize	VERB
ajst-20808	106	19	the	the	DET
ajst-20808	106	20	policy	policy	NOUN
ajst-20808	106	21	function.trpo	function.trpo	PROPN
ajst-20808	106	22	uses	use	VERB
ajst-20808	106	23	relative	relative	ADJ
ajst-20808	106	24	entropy	entropy	NOUN
ajst-20808	106	25	constraints	constraint	NOUN
ajst-20808	106	26	to	to	PART
ajst-20808	106	27	ensure	ensure	VERB
ajst-20808	106	28	the	the	DET
ajst-20808	106	29	stability	stability	NOUN
ajst-20808	106	30	of	of	ADP
ajst-20808	106	31	the	the	DET
ajst-20808	106	32	policy	policy	NOUN
ajst-20808	106	33	updates	update	VERB
ajst-20808	106	34	,	,	PUNCT
ajst-20808	106	35	while	while	SCONJ
ajst-20808	106	36	ppo	ppo	PROPN
ajst-20808	106	37	uses	use	VERB
ajst-20808	106	38	proximal	proximal	ADJ
ajst-20808	106	39	policy	policy	NOUN
ajst-20808	106	40	optimization	optimization	NOUN
ajst-20808	106	41	to	to	PART
ajst-20808	106	42	achieve	achieve	VERB
ajst-20808	106	43	similar	similar	ADJ
ajst-20808	106	44	goals	goal	NOUN
ajst-20808	106	45	.	.	PUNCT
ajst-20808	107	1	in	in	ADP
ajst-20808	107	2	terms	term	NOUN
ajst-20808	107	3	of	of	ADP
ajst-20808	107	4	170	170	NUM
ajst-20808	107	5	performance	performance	NOUN
ajst-20808	107	6	and	and	CCONJ
ajst-20808	107	7	stability	stability	NOUN
ajst-20808	107	8	,	,	PUNCT
ajst-20808	107	9	both	both	PRON
ajst-20808	107	10	usually	usually	ADV
ajst-20808	107	11	have	have	VERB
ajst-20808	107	12	similar	similar	ADJ
ajst-20808	107	13	performance	performance	NOUN
ajst-20808	107	14	,	,	PUNCT
ajst-20808	107	15	but	but	CCONJ
ajst-20808	107	16	ppo	ppo	PROPN
ajst-20808	107	17	is	be	AUX
ajst-20808	107	18	easier	easy	ADJ
ajst-20808	107	19	to	to	PART
ajst-20808	107	20	implement	implement	VERB
ajst-20808	107	21	and	and	CCONJ
ajst-20808	107	22	tune	tune	NOUN
ajst-20808	107	23	.	.	PUNCT
ajst-20808	108	1	monte	monte	PROPN
ajst-20808	108	2	carlo	carlo	PROPN
ajst-20808	108	3	tree	tree	NOUN
ajst-20808	108	4	search	search	NOUN
ajst-20808	108	5	(	(	PUNCT
ajst-20808	108	6	mcts	mct	NOUN
ajst-20808	108	7	)	)	PUNCT
ajst-20808	108	8	and	and	CCONJ
ajst-20808	108	9	ddpg	ddpg	ADJ
ajst-20808	108	10	are	be	AUX
ajst-20808	108	11	both	both	PRON
ajst-20808	108	12	methods	method	NOUN
ajst-20808	108	13	for	for	ADP
ajst-20808	108	14	dealing	deal	VERB
ajst-20808	108	15	with	with	ADP
ajst-20808	108	16	continuous	continuous	ADJ
ajst-20808	108	17	action	action	NOUN
ajst-20808	108	18	spaces	space	NOUN
ajst-20808	108	19	,	,	PUNCT
ajst-20808	108	20	but	but	CCONJ
ajst-20808	108	21	differ	differ	VERB
ajst-20808	108	22	in	in	ADP
ajst-20808	108	23	policy	policy	NOUN
ajst-20808	108	24	search	search	NOUN
ajst-20808	108	25	and	and	CCONJ
ajst-20808	108	26	action	action	NOUN
ajst-20808	108	27	selection.mcts	selection.mct	NOUN
ajst-20808	108	28	is	be	AUX
ajst-20808	108	29	a	a	DET
ajst-20808	108	30	heuristic	heuristic	ADJ
ajst-20808	108	31	search	search	NOUN
ajst-20808	108	32	method	method	NOUN
ajst-20808	108	33	that	that	PRON
ajst-20808	108	34	allows	allow	VERB
ajst-20808	108	35	policy	policy	NOUN
ajst-20808	108	36	search	search	NOUN
ajst-20808	108	37	based	base	VERB
ajst-20808	108	38	on	on	ADP
ajst-20808	108	39	rewards	reward	NOUN
ajst-20808	108	40	and	and	CCONJ
ajst-20808	108	41	state	state	NOUN
ajst-20808	108	42	values	value	NOUN
ajst-20808	108	43	in	in	ADP
ajst-20808	108	44	a	a	DET
ajst-20808	108	45	search	search	NOUN
ajst-20808	108	46	tree	tree	NOUN
ajst-20808	108	47	,	,	PUNCT
ajst-20808	108	48	while	while	SCONJ
ajst-20808	108	49	ddpg	ddpg	ADJ
ajst-20808	108	50	is	be	AUX
ajst-20808	108	51	a	a	DET
ajst-20808	108	52	value	value	NOUN
ajst-20808	108	53	functionbased	functionbase	VERB
ajst-20808	108	54	method	method	NOUN
ajst-20808	108	55	that	that	PRON
ajst-20808	108	56	learns	learn	VERB
ajst-20808	108	57	optimal	optimal	ADJ
ajst-20808	108	58	policies	policy	NOUN
ajst-20808	108	59	directly	directly	ADV
ajst-20808	108	60	.	.	PUNCT
ajst-20808	109	1	mcts	mct	NOUN
ajst-20808	109	2	may	may	AUX
ajst-20808	109	3	perform	perform	VERB
ajst-20808	109	4	better	well	ADV
ajst-20808	109	5	on	on	ADP
ajst-20808	109	6	problems	problem	NOUN
ajst-20808	109	7	that	that	PRON
ajst-20808	109	8	require	require	VERB
ajst-20808	109	9	long	long	ADJ
ajst-20808	109	10	-	-	PUNCT
ajst-20808	109	11	term	term	NOUN
ajst-20808	109	12	planning	planning	NOUN
ajst-20808	109	13	and	and	CCONJ
ajst-20808	109	14	exploration	exploration	NOUN
ajst-20808	109	15	,	,	PUNCT
ajst-20808	109	16	while	while	SCONJ
ajst-20808	109	17	ddpg	ddpg	ADJ
ajst-20808	109	18	is	be	AUX
ajst-20808	109	19	more	more	ADV
ajst-20808	109	20	suitable	suitable	ADJ
ajst-20808	109	21	for	for	ADP
ajst-20808	109	22	continuous	continuous	ADJ
ajst-20808	109	23	action	action	NOUN
ajst-20808	109	24	space	space	NOUN
ajst-20808	109	25	problems	problem	NOUN
ajst-20808	109	26	.	.	PUNCT
ajst-20808	110	1	4.2	4.2	NUM
ajst-20808	110	2	.	.	PUNCT
ajst-20808	111	1	analysis	analysis	NOUN
ajst-20808	111	2	of	of	ADP
ajst-20808	111	3	algorithm	algorithm	NOUN
ajst-20808	111	4	advantages	advantage	NOUN
ajst-20808	111	5	and	and	CCONJ
ajst-20808	111	6	disadvantages	disadvantage	NOUN
ajst-20808	111	7	value	value	NOUN
ajst-20808	111	8	function	function	NOUN
ajst-20808	111	9	based	base	VERB
ajst-20808	111	10	approaches	approach	NOUN
ajst-20808	111	11	for	for	ADP
ajst-20808	111	12	discrete	discrete	ADJ
ajst-20808	111	13	state	state	NOUN
ajst-20808	111	14	and	and	CCONJ
ajst-20808	111	15	action	action	NOUN
ajst-20808	111	16	space	space	NOUN
ajst-20808	111	17	problems	problem	NOUN
ajst-20808	111	18	,	,	PUNCT
ajst-20808	111	19	such	such	ADJ
ajst-20808	111	20	as	as	ADP
ajst-20808	111	21	grid	grid	NOUN
ajst-20808	111	22	worlds	world	NOUN
ajst-20808	111	23	,	,	PUNCT
ajst-20808	111	24	value	value	NOUN
ajst-20808	111	25	function	function	NOUN
ajst-20808	111	26	based	base	VERB
ajst-20808	111	27	approaches	approach	NOUN
ajst-20808	111	28	usually	usually	ADV
ajst-20808	111	29	have	have	VERB
ajst-20808	111	30	better	well	ADJ
ajst-20808	111	31	convergence	convergence	NOUN
ajst-20808	111	32	and	and	CCONJ
ajst-20808	111	33	stability	stability	NOUN
ajst-20808	111	34	.	.	PUNCT
ajst-20808	112	1	the	the	DET
ajst-20808	112	2	value	value	NOUN
ajst-20808	112	3	functions	function	NOUN
ajst-20808	112	4	of	of	ADP
ajst-20808	112	5	states	state	NOUN
ajst-20808	112	6	or	or	CCONJ
ajst-20808	112	7	state	state	NOUN
ajst-20808	112	8	-	-	PUNCT
ajst-20808	112	9	action	action	NOUN
ajst-20808	112	10	pairs	pair	NOUN
ajst-20808	112	11	can	can	AUX
ajst-20808	112	12	be	be	AUX
ajst-20808	112	13	learned	learn	VERB
ajst-20808	112	14	directly	directly	ADV
ajst-20808	112	15	,	,	PUNCT
ajst-20808	112	16	leading	lead	VERB
ajst-20808	112	17	to	to	ADP
ajst-20808	112	18	a	a	DET
ajst-20808	112	19	better	well	ADJ
ajst-20808	112	20	understanding	understanding	NOUN
ajst-20808	112	21	of	of	ADP
ajst-20808	112	22	the	the	DET
ajst-20808	112	23	value	value	NOUN
ajst-20808	112	24	of	of	ADP
ajst-20808	112	25	the	the	DET
ajst-20808	112	26	environment	environment	NOUN
ajst-20808	112	27	and	and	CCONJ
ajst-20808	112	28	actions	action	NOUN
ajst-20808	112	29	.	.	PUNCT
ajst-20808	113	1	however	however	ADV
ajst-20808	113	2	,	,	PUNCT
ajst-20808	113	3	when	when	SCONJ
ajst-20808	113	4	dealing	deal	VERB
ajst-20808	113	5	with	with	ADP
ajst-20808	113	6	continuous	continuous	ADJ
ajst-20808	113	7	state	state	NOUN
ajst-20808	113	8	and	and	CCONJ
ajst-20808	113	9	action	action	NOUN
ajst-20808	113	10	space	space	NOUN
ajst-20808	113	11	problems	problem	NOUN
ajst-20808	113	12	,	,	PUNCT
ajst-20808	113	13	such	such	ADJ
ajst-20808	113	14	as	as	ADP
ajst-20808	113	15	real	real	ADJ
ajst-20808	113	16	robot	robot	NOUN
ajst-20808	113	17	control	control	NOUN
ajst-20808	113	18	,	,	PUNCT
ajst-20808	113	19	the	the	DET
ajst-20808	113	20	value	value	NOUN
ajst-20808	113	21	function	function	NOUN
ajst-20808	113	22	-	-	PUNCT
ajst-20808	113	23	based	base	VERB
ajst-20808	113	24	methods	method	NOUN
ajst-20808	113	25	may	may	AUX
ajst-20808	113	26	suffer	suffer	VERB
ajst-20808	113	27	from	from	ADP
ajst-20808	113	28	dimensional	dimensional	ADJ
ajst-20808	113	29	catastrophe	catastrophe	NOUN
ajst-20808	113	30	,	,	PUNCT
ajst-20808	113	31	leading	lead	VERB
ajst-20808	113	32	to	to	ADP
ajst-20808	113	33	learning	learn	VERB
ajst-20808	113	34	difficulties	difficulty	NOUN
ajst-20808	113	35	and	and	CCONJ
ajst-20808	113	36	performance	performance	NOUN
ajst-20808	113	37	degradation	degradation	NOUN
ajst-20808	113	38	.	.	PUNCT
ajst-20808	114	1	policy	policy	NOUN
ajst-20808	114	2	gradient	gradient	NOUN
ajst-20808	114	3	-	-	PUNCT
ajst-20808	114	4	based	base	VERB
ajst-20808	114	5	methods	method	NOUN
ajst-20808	114	6	can	can	AUX
ajst-20808	114	7	learn	learn	VERB
ajst-20808	114	8	the	the	DET
ajst-20808	114	9	policy	policy	NOUN
ajst-20808	114	10	function	function	NOUN
ajst-20808	114	11	directly	directly	ADV
ajst-20808	114	12	without	without	ADP
ajst-20808	114	13	explicitly	explicitly	ADV
ajst-20808	114	14	estimating	estimate	VERB
ajst-20808	114	15	the	the	DET
ajst-20808	114	16	value	value	NOUN
ajst-20808	114	17	function	function	NOUN
ajst-20808	114	18	,	,	PUNCT
ajst-20808	114	19	thus	thus	ADV
ajst-20808	114	20	enabling	enable	VERB
ajst-20808	114	21	the	the	DET
ajst-20808	114	22	handling	handling	NOUN
ajst-20808	114	23	of	of	ADP
ajst-20808	114	24	continuous	continuous	ADJ
ajst-20808	114	25	action	action	NOUN
ajst-20808	114	26	space	space	NOUN
ajst-20808	114	27	problems	problem	NOUN
ajst-20808	114	28	.	.	PUNCT
ajst-20808	115	1	for	for	ADP
ajst-20808	115	2	highly	highly	ADV
ajst-20808	115	3	stochastic	stochastic	ADJ
ajst-20808	115	4	environments	environment	NOUN
ajst-20808	115	5	and	and	CCONJ
ajst-20808	115	6	tasks	task	NOUN
ajst-20808	115	7	,	,	PUNCT
ajst-20808	115	8	policy	policy	NOUN
ajst-20808	115	9	gradient	gradient	NOUN
ajst-20808	115	10	methods	method	NOUN
ajst-20808	115	11	are	be	AUX
ajst-20808	115	12	usually	usually	ADV
ajst-20808	115	13	more	more	ADV
ajst-20808	115	14	robust	robust	ADJ
ajst-20808	115	15	and	and	CCONJ
ajst-20808	115	16	stable	stable	ADJ
ajst-20808	115	17	.	.	PUNCT
ajst-20808	116	1	however	however	ADV
ajst-20808	116	2	,	,	PUNCT
ajst-20808	116	3	they	they	PRON
ajst-20808	116	4	are	be	AUX
ajst-20808	116	5	less	less	ADV
ajst-20808	116	6	efficient	efficient	ADJ
ajst-20808	116	7	in	in	ADP
ajst-20808	116	8	sample	sample	NOUN
ajst-20808	116	9	utilization	utilization	NOUN
ajst-20808	116	10	during	during	ADP
ajst-20808	116	11	training	training	NOUN
ajst-20808	116	12	and	and	CCONJ
ajst-20808	116	13	may	may	AUX
ajst-20808	116	14	require	require	VERB
ajst-20808	116	15	more	more	ADJ
ajst-20808	116	16	samples	sample	NOUN
ajst-20808	116	17	and	and	CCONJ
ajst-20808	116	18	training	training	NOUN
ajst-20808	116	19	time	time	NOUN
ajst-20808	116	20	to	to	PART
ajst-20808	116	21	obtain	obtain	VERB
ajst-20808	116	22	good	good	ADJ
ajst-20808	116	23	performance	performance	NOUN
ajst-20808	116	24	.	.	PUNCT
ajst-20808	117	1	due	due	ADP
ajst-20808	117	2	to	to	ADP
ajst-20808	117	3	the	the	DET
ajst-20808	117	4	direct	direct	ADJ
ajst-20808	117	5	optimization	optimization	NOUN
ajst-20808	117	6	of	of	ADP
ajst-20808	117	7	the	the	DET
ajst-20808	117	8	strategy	strategy	NOUN
ajst-20808	117	9	,	,	PUNCT
ajst-20808	117	10	it	it	PRON
ajst-20808	117	11	is	be	AUX
ajst-20808	117	12	easy	easy	ADJ
ajst-20808	117	13	to	to	PART
ajst-20808	117	14	be	be	AUX
ajst-20808	117	15	affected	affect	VERB
ajst-20808	117	16	by	by	ADP
ajst-20808	117	17	hyperparameters	hyperparameter	NOUN
ajst-20808	117	18	such	such	ADJ
ajst-20808	117	19	as	as	ADP
ajst-20808	117	20	the	the	DET
ajst-20808	117	21	initialization	initialization	NOUN
ajst-20808	117	22	of	of	ADP
ajst-20808	117	23	the	the	DET
ajst-20808	117	24	strategy	strategy	NOUN
ajst-20808	117	25	parameters	parameter	NOUN
ajst-20808	117	26	and	and	CCONJ
ajst-20808	117	27	the	the	DET
ajst-20808	117	28	update	update	NOUN
ajst-20808	117	29	step	step	NOUN
ajst-20808	117	30	size	size	NOUN
ajst-20808	117	31	,	,	PUNCT
ajst-20808	117	32	and	and	CCONJ
ajst-20808	117	33	it	it	PRON
ajst-20808	117	34	is	be	AUX
ajst-20808	117	35	more	more	ADV
ajst-20808	117	36	difficult	difficult	ADJ
ajst-20808	117	37	to	to	PART
ajst-20808	117	38	tune	tune	VERB
ajst-20808	117	39	the	the	DET
ajst-20808	117	40	parameters	parameter	NOUN
ajst-20808	117	41	.	.	PUNCT
ajst-20808	118	1	the	the	DET
ajst-20808	118	2	value	value	NOUN
ajst-20808	118	3	and	and	CCONJ
ajst-20808	118	4	strategy	strategy	NOUN
ajst-20808	118	5	-	-	PUNCT
ajst-20808	118	6	based	base	VERB
ajst-20808	118	7	approach	approach	NOUN
ajst-20808	118	8	combines	combine	VERB
ajst-20808	118	9	the	the	DET
ajst-20808	118	10	advantages	advantage	NOUN
ajst-20808	118	11	of	of	ADP
ajst-20808	118	12	the	the	DET
ajst-20808	118	13	value	value	NOUN
ajst-20808	118	14	function	function	NOUN
ajst-20808	118	15	-	-	PUNCT
ajst-20808	118	16	based	base	VERB
ajst-20808	118	17	and	and	CCONJ
ajst-20808	118	18	strategy	strategy	NOUN
ajst-20808	118	19	-	-	PUNCT
ajst-20808	118	20	based	base	VERB
ajst-20808	118	21	approaches	approach	NOUN
ajst-20808	118	22	,	,	PUNCT
ajst-20808	118	23	which	which	PRON
ajst-20808	118	24	can	can	AUX
ajst-20808	118	25	effectively	effectively	ADV
ajst-20808	118	26	deal	deal	VERB
ajst-20808	118	27	with	with	ADP
ajst-20808	118	28	the	the	DET
ajst-20808	118	29	continuous	continuous	ADJ
ajst-20808	118	30	state	state	NOUN
ajst-20808	118	31	and	and	CCONJ
ajst-20808	118	32	action	action	NOUN
ajst-20808	118	33	space	space	NOUN
ajst-20808	118	34	problems	problem	NOUN
ajst-20808	118	35	,	,	PUNCT
ajst-20808	118	36	and	and	CCONJ
ajst-20808	118	37	has	have	AUX
ajst-20808	118	38	better	well	ADV
ajst-20808	118	39	learning	learn	VERB
ajst-20808	118	40	performance	performance	NOUN
ajst-20808	118	41	and	and	CCONJ
ajst-20808	118	42	stability	stability	NOUN
ajst-20808	118	43	.	.	PUNCT
ajst-20808	119	1	the	the	DET
ajst-20808	119	2	value	value	NOUN
ajst-20808	119	3	function	function	NOUN
ajst-20808	119	4	and	and	CCONJ
ajst-20808	119	5	the	the	DET
ajst-20808	119	6	strategy	strategy	NOUN
ajst-20808	119	7	function	function	NOUN
ajst-20808	119	8	can	can	AUX
ajst-20808	119	9	be	be	AUX
ajst-20808	119	10	learned	learn	VERB
ajst-20808	119	11	at	at	ADP
ajst-20808	119	12	the	the	DET
ajst-20808	119	13	same	same	ADJ
ajst-20808	119	14	time	time	NOUN
ajst-20808	119	15	,	,	PUNCT
ajst-20808	119	16	leading	lead	VERB
ajst-20808	119	17	to	to	ADP
ajst-20808	119	18	a	a	DET
ajst-20808	119	19	more	more	ADV
ajst-20808	119	20	comprehensive	comprehensive	ADJ
ajst-20808	119	21	understanding	understanding	NOUN
ajst-20808	119	22	of	of	ADP
ajst-20808	119	23	the	the	DET
ajst-20808	119	24	environment	environment	NOUN
ajst-20808	119	25	and	and	CCONJ
ajst-20808	119	26	the	the	DET
ajst-20808	119	27	behavior	behavior	NOUN
ajst-20808	119	28	of	of	ADP
ajst-20808	119	29	the	the	DET
ajst-20808	119	30	intelligences	intelligence	NOUN
ajst-20808	119	31	.	.	PUNCT
ajst-20808	120	1	however	however	ADV
ajst-20808	120	2	,	,	PUNCT
ajst-20808	120	3	the	the	DET
ajst-20808	120	4	algorithm	algorithm	NOUN
ajst-20808	120	5	is	be	AUX
ajst-20808	120	6	more	more	ADV
ajst-20808	120	7	complex	complex	ADJ
ajst-20808	120	8	and	and	CCONJ
ajst-20808	120	9	requires	require	VERB
ajst-20808	120	10	more	more	ADJ
ajst-20808	120	11	computational	computational	ADJ
ajst-20808	120	12	resources	resource	NOUN
ajst-20808	120	13	and	and	CCONJ
ajst-20808	120	14	training	training	NOUN
ajst-20808	120	15	time	time	NOUN
ajst-20808	120	16	to	to	PART
ajst-20808	120	17	obtain	obtain	VERB
ajst-20808	120	18	good	good	ADJ
ajst-20808	120	19	performance	performance	NOUN
ajst-20808	120	20	,	,	PUNCT
ajst-20808	120	21	and	and	CCONJ
ajst-20808	120	22	the	the	DET
ajst-20808	120	23	algorithm	algorithm	NOUN
ajst-20808	120	24	parameters	parameter	NOUN
ajst-20808	120	25	and	and	CCONJ
ajst-20808	120	26	hyperparameters	hyperparameter	NOUN
ajst-20808	120	27	need	need	VERB
ajst-20808	120	28	to	to	PART
ajst-20808	120	29	be	be	AUX
ajst-20808	120	30	carefully	carefully	ADV
ajst-20808	120	31	adjusted	adjust	VERB
ajst-20808	120	32	to	to	PART
ajst-20808	120	33	ensure	ensure	VERB
ajst-20808	120	34	the	the	DET
ajst-20808	120	35	stability	stability	NOUN
ajst-20808	120	36	and	and	CCONJ
ajst-20808	120	37	convergence	convergence	NOUN
ajst-20808	120	38	of	of	ADP
ajst-20808	120	39	the	the	DET
ajst-20808	120	40	algorithm	algorithm	NOUN
ajst-20808	120	41	.	.	PUNCT
ajst-20808	121	1	monte	monte	PROPN
ajst-20808	121	2	carlo	carlo	PROPN
ajst-20808	121	3	tree	tree	NOUN
ajst-20808	121	4	search	search	NOUN
ajst-20808	121	5	(	(	PUNCT
ajst-20808	121	6	mcts	mct	NOUN
ajst-20808	121	7	)	)	PUNCT
ajst-20808	121	8	is	be	AUX
ajst-20808	121	9	suitable	suitable	ADJ
ajst-20808	121	10	for	for	ADP
ajst-20808	121	11	problems	problem	NOUN
ajst-20808	121	12	that	that	PRON
ajst-20808	121	13	require	require	VERB
ajst-20808	121	14	long	long	ADJ
ajst-20808	121	15	-	-	PUNCT
ajst-20808	121	16	term	term	NOUN
ajst-20808	121	17	planning	planning	NOUN
ajst-20808	121	18	and	and	CCONJ
ajst-20808	121	19	exploration	exploration	NOUN
ajst-20808	121	20	,	,	PUNCT
ajst-20808	121	21	and	and	CCONJ
ajst-20808	121	22	can	can	AUX
ajst-20808	121	23	effectively	effectively	ADV
ajst-20808	121	24	search	search	VERB
ajst-20808	121	25	for	for	ADP
ajst-20808	121	26	optimal	optimal	ADJ
ajst-20808	121	27	strategies	strategy	NOUN
ajst-20808	121	28	.	.	PUNCT
ajst-20808	122	1	however	however	ADV
ajst-20808	122	2	,	,	PUNCT
ajst-20808	122	3	the	the	DET
ajst-20808	122	4	computational	computational	ADJ
ajst-20808	122	5	complexity	complexity	NOUN
ajst-20808	122	6	is	be	AUX
ajst-20808	122	7	high	high	ADJ
ajst-20808	122	8	and	and	CCONJ
ajst-20808	122	9	may	may	AUX
ajst-20808	122	10	not	not	PART
ajst-20808	122	11	perform	perform	VERB
ajst-20808	122	12	well	well	ADV
ajst-20808	122	13	on	on	ADP
ajst-20808	122	14	large	large	ADJ
ajst-20808	122	15	-	-	PUNCT
ajst-20808	122	16	scale	scale	NOUN
ajst-20808	122	17	problems	problem	NOUN
ajst-20808	122	18	.	.	PUNCT
ajst-20808	123	1	evolutionary	evolutionary	ADJ
ajst-20808	123	2	methods	method	NOUN
ajst-20808	123	3	(	(	PUNCT
ajst-20808	123	4	ems	ems	PROPN
ajst-20808	123	5	)	)	PUNCT
ajst-20808	123	6	can	can	AUX
ajst-20808	123	7	globally	globally	ADV
ajst-20808	123	8	search	search	VERB
ajst-20808	123	9	the	the	DET
ajst-20808	123	10	policy	policy	NOUN
ajst-20808	123	11	space	space	NOUN
ajst-20808	123	12	and	and	CCONJ
ajst-20808	123	13	are	be	AUX
ajst-20808	123	14	suitable	suitable	ADJ
ajst-20808	123	15	for	for	ADP
ajst-20808	123	16	complex	complex	ADJ
ajst-20808	123	17	and	and	CCONJ
ajst-20808	123	18	high	high	ADJ
ajst-20808	123	19	dimensional	dimensional	ADJ
ajst-20808	123	20	problems	problem	NOUN
ajst-20808	123	21	.	.	PUNCT
ajst-20808	124	1	however	however	ADV
ajst-20808	124	2	,	,	PUNCT
ajst-20808	124	3	the	the	DET
ajst-20808	124	4	convergence	convergence	NOUN
ajst-20808	124	5	speed	speed	NOUN
ajst-20808	124	6	is	be	AUX
ajst-20808	124	7	slow	slow	ADJ
ajst-20808	124	8	,	,	PUNCT
ajst-20808	124	9	sensitive	sensitive	ADJ
ajst-20808	124	10	to	to	ADP
ajst-20808	124	11	algorithm	algorithm	NOUN
ajst-20808	124	12	parameters	parameter	NOUN
ajst-20808	124	13	and	and	CCONJ
ajst-20808	124	14	population	population	NOUN
ajst-20808	124	15	settings	setting	NOUN
ajst-20808	124	16	,	,	PUNCT
ajst-20808	124	17	and	and	CCONJ
ajst-20808	124	18	difficult	difficult	ADJ
ajst-20808	124	19	to	to	PART
ajst-20808	124	20	tune	tune	VERB
ajst-20808	124	21	the	the	DET
ajst-20808	124	22	parameters	parameter	NOUN
ajst-20808	124	23	.	.	PUNCT
ajst-20808	125	1	4.3	4.3	NUM
ajst-20808	125	2	.	.	PUNCT
ajst-20808	125	3	applicable	applicable	ADJ
ajst-20808	125	4	scenarios	scenario	NOUN
ajst-20808	125	5	of	of	ADP
ajst-20808	125	6	algorithms	algorithm	NOUN
ajst-20808	125	7	based	base	VERB
ajst-20808	125	8	on	on	ADP
ajst-20808	125	9	value	value	NOUN
ajst-20808	125	10	function	function	NOUN
ajst-20808	125	11	methods	method	NOUN
ajst-20808	126	1	,	,	PUNCT
ajst-20808	126	2	these	these	DET
ajst-20808	126	3	methods	method	NOUN
ajst-20808	126	4	usually	usually	ADV
ajst-20808	126	5	perform	perform	VERB
ajst-20808	126	6	well	well	ADV
ajst-20808	126	7	on	on	ADP
ajst-20808	126	8	problems	problem	NOUN
ajst-20808	126	9	with	with	ADP
ajst-20808	126	10	discrete	discrete	ADJ
ajst-20808	126	11	state	state	NOUN
ajst-20808	126	12	and	and	CCONJ
ajst-20808	126	13	action	action	NOUN
ajst-20808	126	14	spaces	space	NOUN
ajst-20808	126	15	,	,	PUNCT
ajst-20808	126	16	such	such	ADJ
ajst-20808	126	17	as	as	ADP
ajst-20808	126	18	board	board	NOUN
ajst-20808	126	19	games	game	NOUN
ajst-20808	126	20	and	and	CCONJ
ajst-20808	126	21	mazes	maze	NOUN
ajst-20808	126	22	.	.	PUNCT
ajst-20808	127	1	since	since	SCONJ
ajst-20808	127	2	these	these	DET
ajst-20808	127	3	problems	problem	NOUN
ajst-20808	127	4	have	have	VERB
ajst-20808	127	5	clear	clear	ADJ
ajst-20808	127	6	state	state	NOUN
ajst-20808	127	7	and	and	CCONJ
ajst-20808	127	8	action	action	NOUN
ajst-20808	127	9	definitions	definition	NOUN
ajst-20808	127	10	,	,	PUNCT
ajst-20808	127	11	value	value	NOUN
ajst-20808	127	12	function	function	NOUN
ajst-20808	127	13	-	-	PUNCT
ajst-20808	127	14	based	base	VERB
ajst-20808	127	15	methods	method	NOUN
ajst-20808	127	16	can	can	AUX
ajst-20808	127	17	effectively	effectively	ADV
ajst-20808	127	18	learn	learn	VERB
ajst-20808	127	19	and	and	CCONJ
ajst-20808	127	20	infer	infer	VERB
ajst-20808	127	21	optimal	optimal	ADJ
ajst-20808	127	22	policies	policy	NOUN
ajst-20808	127	23	.	.	PUNCT
ajst-20808	128	1	for	for	ADP
ajst-20808	128	2	example	example	NOUN
ajst-20808	128	3	,	,	PUNCT
ajst-20808	128	4	q	q	NOUN
ajst-20808	128	5	-	-	PUNCT
ajst-20808	128	6	learning	learning	NOUN
ajst-20808	128	7	and	and	CCONJ
ajst-20808	128	8	sarsa	sarsa	NOUN
ajst-20808	128	9	usually	usually	ADV
ajst-20808	128	10	achieve	achieve	VERB
ajst-20808	128	11	good	good	ADJ
ajst-20808	128	12	performance	performance	NOUN
ajst-20808	128	13	in	in	ADP
ajst-20808	128	14	such	such	ADJ
ajst-20808	128	15	problems	problem	NOUN
ajst-20808	128	16	because	because	SCONJ
ajst-20808	128	17	they	they	PRON
ajst-20808	128	18	are	be	AUX
ajst-20808	128	19	able	able	ADJ
ajst-20808	128	20	to	to	PART
ajst-20808	128	21	achieve	achieve	VERB
ajst-20808	128	22	policy	policy	NOUN
ajst-20808	128	23	optimization	optimization	NOUN
ajst-20808	128	24	by	by	ADP
ajst-20808	128	25	estimating	estimate	VERB
ajst-20808	128	26	the	the	DET
ajst-20808	128	27	value	value	NOUN
ajst-20808	128	28	functions	function	NOUN
ajst-20808	128	29	of	of	ADP
ajst-20808	128	30	states	state	NOUN
ajst-20808	128	31	or	or	CCONJ
ajst-20808	128	32	pairs	pair	NOUN
ajst-20808	128	33	of	of	ADP
ajst-20808	128	34	state	state	NOUN
ajst-20808	128	35	-	-	PUNCT
ajst-20808	128	36	action	action	NOUN
ajst-20808	128	37	pairs	pair	NOUN
ajst-20808	128	38	.	.	PUNCT
ajst-20808	129	1	policy	policy	NOUN
ajst-20808	129	2	gradient	gradient	NOUN
ajst-20808	129	3	-	-	PUNCT
ajst-20808	129	4	based	base	VERB
ajst-20808	129	5	methods	method	NOUN
ajst-20808	129	6	are	be	AUX
ajst-20808	129	7	mainly	mainly	ADV
ajst-20808	129	8	applicable	applicable	ADJ
ajst-20808	129	9	to	to	ADP
ajst-20808	129	10	problems	problem	NOUN
ajst-20808	129	11	with	with	ADP
ajst-20808	129	12	continuous	continuous	ADJ
ajst-20808	129	13	action	action	NOUN
ajst-20808	129	14	spaces	space	NOUN
ajst-20808	129	15	,	,	PUNCT
ajst-20808	129	16	such	such	ADJ
ajst-20808	129	17	as	as	ADP
ajst-20808	129	18	robot	robot	NOUN
ajst-20808	129	19	control	control	NOUN
ajst-20808	129	20	and	and	CCONJ
ajst-20808	129	21	continuous	continuous	ADJ
ajst-20808	129	22	control	control	NOUN
ajst-20808	129	23	systems	system	NOUN
ajst-20808	129	24	.	.	PUNCT
ajst-20808	130	1	since	since	SCONJ
ajst-20808	130	2	the	the	DET
ajst-20808	130	3	action	action	NOUN
ajst-20808	130	4	space	space	NOUN
ajst-20808	130	5	of	of	ADP
ajst-20808	130	6	these	these	DET
ajst-20808	130	7	problems	problem	NOUN
ajst-20808	130	8	is	be	AUX
ajst-20808	130	9	continuous	continuous	ADJ
ajst-20808	130	10	,	,	PUNCT
ajst-20808	130	11	policy	policy	NOUN
ajst-20808	130	12	gradient	gradient	NOUN
ajst-20808	130	13	-	-	PUNCT
ajst-20808	130	14	based	base	VERB
ajst-20808	130	15	methods	method	NOUN
ajst-20808	130	16	are	be	AUX
ajst-20808	130	17	able	able	ADJ
ajst-20808	130	18	to	to	PART
ajst-20808	130	19	learn	learn	VERB
ajst-20808	130	20	the	the	DET
ajst-20808	130	21	policy	policy	NOUN
ajst-20808	130	22	function	function	NOUN
ajst-20808	130	23	directly	directly	ADV
ajst-20808	130	24	with	with	ADP
ajst-20808	130	25	good	good	ADJ
ajst-20808	130	26	generalization	generalization	NOUN
ajst-20808	130	27	ability	ability	NOUN
ajst-20808	130	28	and	and	CCONJ
ajst-20808	130	29	robustness	robustness	NOUN
ajst-20808	130	30	.	.	PUNCT
ajst-20808	131	1	for	for	ADP
ajst-20808	131	2	example	example	NOUN
ajst-20808	131	3	,	,	PUNCT
ajst-20808	131	4	reinforce	reinforce	VERB
ajst-20808	131	5	and	and	CCONJ
ajst-20808	131	6	ddpg	ddpg	ADJ
ajst-20808	131	7	typically	typically	ADV
ajst-20808	131	8	achieve	achieve	VERB
ajst-20808	131	9	better	well	ADJ
ajst-20808	131	10	performance	performance	NOUN
ajst-20808	131	11	in	in	ADP
ajst-20808	131	12	such	such	ADJ
ajst-20808	131	13	problems	problem	NOUN
ajst-20808	131	14	because	because	SCONJ
ajst-20808	131	15	they	they	PRON
ajst-20808	131	16	are	be	AUX
ajst-20808	131	17	able	able	ADJ
ajst-20808	131	18	to	to	PART
ajst-20808	131	19	update	update	VERB
ajst-20808	131	20	their	their	PRON
ajst-20808	131	21	strategies	strategy	NOUN
ajst-20808	131	22	in	in	ADP
ajst-20808	131	23	real	real	ADJ
ajst-20808	131	24	time	time	NOUN
ajst-20808	131	25	and	and	CCONJ
ajst-20808	131	26	adapt	adapt	VERB
ajst-20808	131	27	to	to	ADP
ajst-20808	131	28	continuous	continuous	ADJ
ajst-20808	131	29	environments	environment	NOUN
ajst-20808	131	30	.	.	PUNCT
ajst-20808	132	1	valueand	valueand	ADJ
ajst-20808	132	2	policy	policy	NOUN
ajst-20808	132	3	-	-	PUNCT
ajst-20808	132	4	based	base	VERB
ajst-20808	132	5	methods	method	NOUN
ajst-20808	132	6	combine	combine	VERB
ajst-20808	132	7	the	the	DET
ajst-20808	132	8	advantages	advantage	NOUN
ajst-20808	132	9	of	of	ADP
ajst-20808	132	10	value	value	NOUN
ajst-20808	132	11	function	function	NOUN
ajst-20808	132	12	-	-	PUNCT
ajst-20808	132	13	based	base	VERB
ajst-20808	132	14	and	and	CCONJ
ajst-20808	132	15	policy	policy	NOUN
ajst-20808	132	16	-	-	PUNCT
ajst-20808	132	17	based	base	VERB
ajst-20808	132	18	methods	method	NOUN
ajst-20808	132	19	,	,	PUNCT
ajst-20808	132	20	and	and	CCONJ
ajst-20808	132	21	are	be	AUX
ajst-20808	132	22	able	able	ADJ
ajst-20808	132	23	to	to	PART
ajst-20808	132	24	handle	handle	VERB
ajst-20808	132	25	more	more	ADJ
ajst-20808	132	26	complex	complex	ADJ
ajst-20808	132	27	problems	problem	NOUN
ajst-20808	132	28	with	with	ADP
ajst-20808	132	29	better	well	ADJ
ajst-20808	132	30	learning	learn	VERB
ajst-20808	132	31	performance	performance	NOUN
ajst-20808	132	32	and	and	CCONJ
ajst-20808	132	33	stability	stability	NOUN
ajst-20808	132	34	.	.	PUNCT
ajst-20808	133	1	this	this	DET
ajst-20808	133	2	type	type	NOUN
ajst-20808	133	3	of	of	ADP
ajst-20808	133	4	approach	approach	NOUN
ajst-20808	133	5	is	be	AUX
ajst-20808	133	6	suitable	suitable	ADJ
ajst-20808	133	7	for	for	ADP
ajst-20808	133	8	problems	problem	NOUN
ajst-20808	133	9	with	with	ADP
ajst-20808	133	10	both	both	CCONJ
ajst-20808	133	11	discrete	discrete	ADJ
ajst-20808	133	12	and	and	CCONJ
ajst-20808	133	13	continuous	continuous	ADJ
ajst-20808	133	14	state	state	NOUN
ajst-20808	133	15	spaces	space	NOUN
ajst-20808	133	16	or	or	CCONJ
ajst-20808	133	17	action	action	NOUN
ajst-20808	133	18	spaces	space	NOUN
ajst-20808	133	19	.	.	PUNCT
ajst-20808	134	1	for	for	ADP
ajst-20808	134	2	example	example	NOUN
ajst-20808	134	3	,	,	PUNCT
ajst-20808	134	4	ppo	ppo	PROPN
ajst-20808	134	5	(	(	PUNCT
ajst-20808	134	6	proximal	proximal	ADJ
ajst-20808	134	7	policy	policy	NOUN
ajst-20808	134	8	optimization	optimization	NOUN
ajst-20808	134	9	)	)	PUNCT
ajst-20808	134	10	and	and	CCONJ
ajst-20808	134	11	a3c	a3c	NUM
ajst-20808	134	12	(	(	PUNCT
ajst-20808	134	13	asynchronous	asynchronous	ADJ
ajst-20808	134	14	advantageous	advantageous	ADJ
ajst-20808	134	15	actorcommentator	actorcommentator	NOUN
ajst-20808	134	16	algorithm	algorithm	NOUN
ajst-20808	134	17	)	)	PUNCT
ajst-20808	134	18	usually	usually	ADV
ajst-20808	134	19	achieve	achieve	VERB
ajst-20808	134	20	good	good	ADJ
ajst-20808	134	21	performance	performance	NOUN
ajst-20808	134	22	in	in	ADP
ajst-20808	134	23	such	such	ADJ
ajst-20808	134	24	problems	problem	NOUN
ajst-20808	134	25	because	because	SCONJ
ajst-20808	134	26	they	they	PRON
ajst-20808	134	27	are	be	AUX
ajst-20808	134	28	able	able	ADJ
ajst-20808	134	29	to	to	PART
ajst-20808	134	30	learn	learn	VERB
ajst-20808	134	31	both	both	CCONJ
ajst-20808	134	32	the	the	DET
ajst-20808	134	33	value	value	NOUN
ajst-20808	134	34	function	function	NOUN
ajst-20808	134	35	and	and	CCONJ
ajst-20808	134	36	the	the	DET
ajst-20808	134	37	policy	policy	NOUN
ajst-20808	134	38	function	function	NOUN
ajst-20808	134	39	,	,	PUNCT
ajst-20808	134	40	and	and	CCONJ
ajst-20808	134	41	understand	understand	VERB
ajst-20808	134	42	the	the	DET
ajst-20808	134	43	environment	environment	NOUN
ajst-20808	134	44	and	and	CCONJ
ajst-20808	134	45	the	the	DET
ajst-20808	134	46	intelligence	intelligence	NOUN
ajst-20808	134	47	's	's	PART
ajst-20808	134	48	behaviors	behavior	NOUN
ajst-20808	134	49	more	more	ADV
ajst-20808	134	50	comprehensively	comprehensively	ADV
ajst-20808	134	51	.	.	PUNCT
ajst-20808	135	1	monte	monte	PROPN
ajst-20808	135	2	carlo	carlo	PROPN
ajst-20808	135	3	tree	tree	NOUN
ajst-20808	135	4	search	search	NOUN
ajst-20808	135	5	(	(	PUNCT
ajst-20808	135	6	mcts	mct	NOUN
ajst-20808	135	7	)	)	PUNCT
ajst-20808	135	8	is	be	AUX
ajst-20808	135	9	suitable	suitable	ADJ
ajst-20808	135	10	for	for	ADP
ajst-20808	135	11	problems	problem	NOUN
ajst-20808	135	12	that	that	PRON
ajst-20808	135	13	require	require	VERB
ajst-20808	135	14	long	long	ADJ
ajst-20808	135	15	term	term	NOUN
ajst-20808	135	16	planning	planning	NOUN
ajst-20808	135	17	and	and	CCONJ
ajst-20808	135	18	exploration	exploration	NOUN
ajst-20808	135	19	,	,	PUNCT
ajst-20808	135	20	such	such	ADJ
ajst-20808	135	21	as	as	ADP
ajst-20808	135	22	go	go	VERB
ajst-20808	135	23	,	,	PUNCT
ajst-20808	135	24	poker	poker	NOUN
ajst-20808	135	25	,	,	PUNCT
ajst-20808	135	26	etc	etc	X
ajst-20808	135	27	.	.	X
ajst-20808	136	1	mcts	mct	NOUN
ajst-20808	136	2	usually	usually	ADV
ajst-20808	136	3	achieves	achieve	VERB
ajst-20808	136	4	good	good	ADJ
ajst-20808	136	5	performance	performance	NOUN
ajst-20808	136	6	in	in	ADP
ajst-20808	136	7	these	these	DET
ajst-20808	136	8	problems	problem	NOUN
ajst-20808	136	9	because	because	SCONJ
ajst-20808	136	10	it	it	PRON
ajst-20808	136	11	enables	enable	VERB
ajst-20808	136	12	long	long	ADJ
ajst-20808	136	13	term	term	NOUN
ajst-20808	136	14	planning	planning	NOUN
ajst-20808	136	15	and	and	CCONJ
ajst-20808	136	16	exploration	exploration	NOUN
ajst-20808	136	17	through	through	ADP
ajst-20808	136	18	heuristic	heuristic	ADJ
ajst-20808	136	19	search	search	NOUN
ajst-20808	136	20	.	.	PUNCT
ajst-20808	137	1	evolutionary	evolutionary	ADJ
ajst-20808	137	2	methods	method	NOUN
ajst-20808	137	3	are	be	AUX
ajst-20808	137	4	suitable	suitable	ADJ
ajst-20808	137	5	for	for	ADP
ajst-20808	137	6	problems	problem	NOUN
ajst-20808	137	7	with	with	ADP
ajst-20808	137	8	large	large	ADJ
ajst-20808	137	9	strategy	strategy	NOUN
ajst-20808	137	10	space	space	NOUN
ajst-20808	137	11	and	and	CCONJ
ajst-20808	137	12	high	high	ADJ
ajst-20808	137	13	complexity	complexity	NOUN
ajst-20808	137	14	.	.	PUNCT
ajst-20808	138	1	evolutionary	evolutionary	ADJ
ajst-20808	138	2	methods	method	NOUN
ajst-20808	138	3	usually	usually	ADV
ajst-20808	138	4	achieve	achieve	VERB
ajst-20808	138	5	better	well	ADJ
ajst-20808	138	6	performance	performance	NOUN
ajst-20808	138	7	in	in	ADP
ajst-20808	138	8	these	these	DET
ajst-20808	138	9	problems	problem	NOUN
ajst-20808	138	10	because	because	SCONJ
ajst-20808	138	11	they	they	PRON
ajst-20808	138	12	are	be	AUX
ajst-20808	138	13	able	able	ADJ
ajst-20808	138	14	to	to	PART
ajst-20808	138	15	optimize	optimize	VERB
ajst-20808	138	16	the	the	DET
ajst-20808	138	17	policy	policy	NOUN
ajst-20808	138	18	through	through	ADP
ajst-20808	138	19	global	global	ADJ
ajst-20808	138	20	search	search	NOUN
ajst-20808	138	21	.	.	PUNCT
ajst-20808	139	1	5	5	X
ajst-20808	139	2	.	.	X
ajst-20808	139	3	conclusion	conclusion	NOUN
ajst-20808	139	4	.	.	PUNCT
ajst-20808	140	1	currently	currently	ADV
ajst-20808	140	2	,	,	PUNCT
ajst-20808	140	3	reinforcement	reinforcement	NOUN
ajst-20808	140	4	learning	learning	NOUN
ajst-20808	140	5	methods	method	NOUN
ajst-20808	140	6	play	play	VERB
ajst-20808	140	7	an	an	DET
ajst-20808	140	8	important	important	ADJ
ajst-20808	140	9	role	role	NOUN
ajst-20808	140	10	in	in	ADP
ajst-20808	140	11	the	the	DET
ajst-20808	140	12	field	field	NOUN
ajst-20808	140	13	of	of	ADP
ajst-20808	140	14	artificial	artificial	ADJ
ajst-20808	140	15	intelligence	intelligence	NOUN
ajst-20808	140	16	and	and	CCONJ
ajst-20808	140	17	are	be	AUX
ajst-20808	140	18	widely	widely	ADV
ajst-20808	140	19	used	use	VERB
ajst-20808	140	20	in	in	ADP
ajst-20808	140	21	a	a	DET
ajst-20808	140	22	variety	variety	NOUN
ajst-20808	140	23	of	of	ADP
ajst-20808	140	24	complex	complex	ADJ
ajst-20808	140	25	problem	problem	NOUN
ajst-20808	140	26	domains	domain	NOUN
ajst-20808	140	27	,	,	PUNCT
ajst-20808	140	28	such	such	ADJ
ajst-20808	140	29	as	as	ADP
ajst-20808	140	30	the	the	DET
ajst-20808	140	31	gaming	gaming	NOUN
ajst-20808	140	32	domain	domain	NOUN
ajst-20808	140	33	,	,	PUNCT
ajst-20808	140	34	robot	robot	NOUN
ajst-20808	140	35	control	control	NOUN
ajst-20808	140	36	,	,	PUNCT
ajst-20808	140	37	and	and	CCONJ
ajst-20808	140	38	traffic	traffic	NOUN
ajst-20808	140	39	management	management	NOUN
ajst-20808	140	40	.	.	PUNCT
ajst-20808	141	1	while	while	SCONJ
ajst-20808	141	2	different	different	ADJ
ajst-20808	141	3	reinforcement	reinforcement	NOUN
ajst-20808	141	4	learning	learning	NOUN
ajst-20808	141	5	methods	method	NOUN
ajst-20808	141	6	have	have	VERB
ajst-20808	141	7	their	their	PRON
ajst-20808	141	8	own	own	ADJ
ajst-20808	141	9	strengths	strength	NOUN
ajst-20808	141	10	and	and	CCONJ
ajst-20808	141	11	weaknesses	weakness	NOUN
ajst-20808	141	12	,	,	PUNCT
ajst-20808	141	13	overall	overall	ADV
ajst-20808	141	14	,	,	PUNCT
ajst-20808	141	15	they	they	PRON
ajst-20808	141	16	have	have	AUX
ajst-20808	141	17	made	make	VERB
ajst-20808	141	18	significant	significant	ADJ
ajst-20808	141	19	strides	stride	NOUN
ajst-20808	141	20	in	in	ADP
ajst-20808	141	21	addressing	address	VERB
ajst-20808	141	22	a	a	DET
ajst-20808	141	23	variety	variety	NOUN
ajst-20808	141	24	of	of	ADP
ajst-20808	141	25	highly	highly	ADV
ajst-20808	141	26	uncertain	uncertain	ADJ
ajst-20808	141	27	and	and	CCONJ
ajst-20808	141	28	complex	complex	ADJ
ajst-20808	141	29	decision	decision	NOUN
ajst-20808	141	30	-	-	PUNCT
ajst-20808	141	31	making	make	VERB
ajst-20808	141	32	problems	problem	NOUN
ajst-20808	141	33	.	.	PUNCT
ajst-20808	142	1	for	for	ADP
ajst-20808	142	2	instance	instance	NOUN
ajst-20808	142	3	,	,	PUNCT
ajst-20808	142	4	when	when	SCONJ
ajst-20808	142	5	dealing	deal	VERB
ajst-20808	142	6	with	with	ADP
ajst-20808	142	7	challenges	challenge	NOUN
ajst-20808	142	8	such	such	ADJ
ajst-20808	142	9	as	as	ADP
ajst-20808	142	10	bad	bad	ADJ
ajst-20808	142	11	data	data	NOUN
ajst-20808	142	12	detection	detection	NOUN
ajst-20808	142	13	(	(	PUNCT
ajst-20808	142	14	bdd	bdd	PROPN
ajst-20808	142	15	)	)	PUNCT
ajst-20808	142	16	and	and	CCONJ
ajst-20808	142	17	neural	neural	ADJ
ajst-20808	142	18	attack	attack	NOUN
ajst-20808	142	19	locations	location	NOUN
ajst-20808	142	20	(	(	PUNCT
ajst-20808	142	21	nal	nal	NOUN
ajst-20808	142	22	)	)	PUNCT
ajst-20808	142	23	,	,	PUNCT
ajst-20808	142	24	leveraging	leverage	VERB
ajst-20808	142	25	key	key	ADJ
ajst-20808	142	26	designs[6	designs[6	NOUN
ajst-20808	142	27	]	]	PUNCT
ajst-20808	142	28	like	like	ADP
ajst-20808	142	29	perturbation	perturbation	NOUN
ajst-20808	142	30	of	of	ADP
ajst-20808	142	31	state	state	NOUN
ajst-20808	142	32	variables	variable	NOUN
ajst-20808	142	33	,	,	PUNCT
ajst-20808	142	34	customized	customize	VERB
ajst-20808	142	35	loss	loss	NOUN
ajst-20808	142	36	function	function	NOUN
ajst-20808	142	37	design	design	NOUN
ajst-20808	142	38	,	,	PUNCT
ajst-20808	142	39	and	and	CCONJ
ajst-20808	142	40	variable	variable	ADJ
ajst-20808	142	41	transformation	transformation	NOUN
ajst-20808	142	42	becomes	become	VERB
ajst-20808	142	43	crucial	crucial	ADJ
ajst-20808	142	44	to	to	PART
ajst-20808	142	45	tackle	tackle	VERB
ajst-20808	142	46	real	real	ADJ
ajst-20808	142	47	-	-	PUNCT
ajst-20808	142	48	world	world	NOUN
ajst-20808	142	49	complexities	complexity	NOUN
ajst-20808	142	50	.	.	PUNCT
ajst-20808	143	1	value	value	NOUN
ajst-20808	143	2	function	function	NOUN
ajst-20808	143	3	-	-	PUNCT
ajst-20808	143	4	based	base	VERB
ajst-20808	143	5	methods	method	NOUN
ajst-20808	143	6	perform	perform	VERB
ajst-20808	143	7	well	well	ADV
ajst-20808	143	8	on	on	ADP
ajst-20808	143	9	problems	problem	NOUN
ajst-20808	143	10	with	with	ADP
ajst-20808	143	11	discrete	discrete	ADJ
ajst-20808	143	12	states	state	NOUN
ajst-20808	143	13	and	and	CCONJ
ajst-20808	143	14	action	action	NOUN
ajst-20808	143	15	spaces	space	NOUN
ajst-20808	143	16	,	,	PUNCT
ajst-20808	143	17	and	and	CCONJ
ajst-20808	143	18	are	be	AUX
ajst-20808	143	19	able	able	ADJ
ajst-20808	143	20	to	to	PART
ajst-20808	143	21	achieve	achieve	VERB
ajst-20808	143	22	better	well	ADJ
ajst-20808	143	23	policy	policy	NOUN
ajst-20808	143	24	optimization	optimization	NOUN
ajst-20808	143	25	through	through	ADP
ajst-20808	143	26	value	value	NOUN
ajst-20808	143	27	function	function	NOUN
ajst-20808	143	28	estimation	estimation	NOUN
ajst-20808	143	29	.	.	PUNCT
ajst-20808	144	1	the	the	DET
ajst-20808	144	2	strategy	strategy	NOUN
ajst-20808	144	3	gradient	gradient	NOUN
ajst-20808	144	4	-	-	PUNCT
ajst-20808	144	5	based	base	VERB
ajst-20808	144	6	methods	method	NOUN
ajst-20808	144	7	are	be	AUX
ajst-20808	144	8	suitable	suitable	ADJ
ajst-20808	144	9	for	for	ADP
ajst-20808	144	10	problems	problem	NOUN
ajst-20808	144	11	in	in	ADP
ajst-20808	144	12	continuous	continuous	ADJ
ajst-20808	144	13	action	action	NOUN
ajst-20808	144	14	space	space	NOUN
ajst-20808	144	15	with	with	ADP
ajst-20808	144	16	good	good	ADJ
ajst-20808	144	17	generalization	generalization	NOUN
ajst-20808	144	18	ability	ability	NOUN
ajst-20808	144	19	and	and	CCONJ
ajst-20808	144	20	robustness	robustness	NOUN
ajst-20808	144	21	.	.	PUNCT
ajst-20808	145	1	in	in	ADP
ajst-20808	145	2	contrast	contrast	NOUN
ajst-20808	145	3	,	,	PUNCT
ajst-20808	145	4	the	the	DET
ajst-20808	145	5	value	value	NOUN
ajst-20808	145	6	and	and	CCONJ
ajst-20808	145	7	strategybased	strategybased	ADJ
ajst-20808	145	8	methods	method	NOUN
ajst-20808	145	9	are	be	AUX
ajst-20808	145	10	able	able	ADJ
ajst-20808	145	11	to	to	PART
ajst-20808	145	12	combine	combine	VERB
ajst-20808	145	13	the	the	DET
ajst-20808	145	14	advantages	advantage	NOUN
ajst-20808	145	15	of	of	ADP
ajst-20808	145	16	both	both	PRON
ajst-20808	145	17	to	to	PART
ajst-20808	145	18	deal	deal	VERB
ajst-20808	145	19	with	with	ADP
ajst-20808	145	20	more	more	ADJ
ajst-20808	145	21	complex	complex	ADJ
ajst-20808	145	22	problems	problem	NOUN
ajst-20808	145	23	and	and	CCONJ
ajst-20808	145	24	have	have	AUX
ajst-20808	145	25	better	well	ADV
ajst-20808	145	26	learning	learn	VERB
ajst-20808	145	27	performance	performance	NOUN
ajst-20808	145	28	and	and	CCONJ
ajst-20808	145	29	stability	stability	NOUN
ajst-20808	145	30	.	.	PUNCT
ajst-20808	146	1	despite	despite	SCONJ
ajst-20808	146	2	the	the	DET
ajst-20808	146	3	remarkable	remarkable	ADJ
ajst-20808	146	4	progress	progress	NOUN
ajst-20808	146	5	of	of	ADP
ajst-20808	146	6	reinforcement	reinforcement	NOUN
ajst-20808	146	7	learning	learning	NOUN
ajst-20808	146	8	methods	method	NOUN
ajst-20808	146	9	,	,	PUNCT
ajst-20808	146	10	they	they	PRON
ajst-20808	146	11	still	still	ADV
ajst-20808	146	12	face	face	VERB
ajst-20808	146	13	some	some	DET
ajst-20808	146	14	challenges	challenge	NOUN
ajst-20808	146	15	,	,	PUNCT
ajst-20808	146	16	such	such	ADJ
ajst-20808	146	17	as	as	ADP
ajst-20808	146	18	the	the	DET
ajst-20808	146	19	171	171	NUM
ajst-20808	146	20	convergence	convergence	NOUN
ajst-20808	146	21	speed	speed	NOUN
ajst-20808	146	22	of	of	ADP
ajst-20808	146	23	algorithms	algorithm	NOUN
ajst-20808	146	24	,	,	PUNCT
ajst-20808	146	25	the	the	DET
ajst-20808	146	26	stability	stability	NOUN
ajst-20808	146	27	of	of	ADP
ajst-20808	146	28	the	the	DET
ajst-20808	146	29	training	training	NOUN
ajst-20808	146	30	process	process	NOUN
ajst-20808	146	31	,	,	PUNCT
ajst-20808	146	32	and	and	CCONJ
ajst-20808	146	33	the	the	DET
ajst-20808	146	34	demand	demand	NOUN
ajst-20808	146	35	for	for	ADP
ajst-20808	146	36	computational	computational	ADJ
ajst-20808	146	37	resources	resource	NOUN
ajst-20808	146	38	.	.	PUNCT
ajst-20808	147	1	in	in	ADP
ajst-20808	147	2	the	the	DET
ajst-20808	147	3	future	future	NOUN
ajst-20808	147	4	,	,	PUNCT
ajst-20808	147	5	further	far	ADV
ajst-20808	147	6	in	in	ADP
ajst-20808	147	7	-	-	PUNCT
ajst-20808	147	8	depth	depth	NOUN
ajst-20808	147	9	research	research	NOUN
ajst-20808	147	10	on	on	ADP
ajst-20808	147	11	the	the	DET
ajst-20808	147	12	optimization	optimization	NOUN
ajst-20808	147	13	and	and	CCONJ
ajst-20808	147	14	improvement	improvement	NOUN
ajst-20808	147	15	of	of	ADP
ajst-20808	147	16	various	various	ADJ
ajst-20808	147	17	reinforcement	reinforcement	NOUN
ajst-20808	147	18	learning	learning	NOUN
ajst-20808	147	19	methods	method	NOUN
ajst-20808	147	20	is	be	AUX
ajst-20808	147	21	needed	need	VERB
ajst-20808	147	22	to	to	PART
ajst-20808	147	23	improve	improve	VERB
ajst-20808	147	24	the	the	DET
ajst-20808	147	25	efficiency	efficiency	NOUN
ajst-20808	147	26	and	and	CCONJ
ajst-20808	147	27	reliability	reliability	NOUN
ajst-20808	147	28	of	of	ADP
ajst-20808	147	29	the	the	DET
ajst-20808	147	30	algorithms	algorithm	NOUN
ajst-20808	147	31	in	in	ADP
ajst-20808	147	32	solving	solve	VERB
ajst-20808	147	33	complex	complex	ADJ
ajst-20808	147	34	practical	practical	ADJ
ajst-20808	147	35	problems	problem	NOUN
ajst-20808	147	36	.	.	PUNCT
ajst-20808	148	1	meanwhile	meanwhile	ADV
ajst-20808	148	2	,	,	PUNCT
ajst-20808	148	3	exploring	explore	VERB
ajst-20808	148	4	more	more	ADV
ajst-20808	148	5	efficient	efficient	ADJ
ajst-20808	148	6	and	and	CCONJ
ajst-20808	148	7	flexible	flexible	ADJ
ajst-20808	148	8	reinforcement	reinforcement	NOUN
ajst-20808	148	9	learning	learning	NOUN
ajst-20808	148	10	frameworks	framework	NOUN
ajst-20808	148	11	by	by	ADP
ajst-20808	148	12	combining	combine	VERB
ajst-20808	148	13	deep	deep	ADJ
ajst-20808	148	14	learning	learning	NOUN
ajst-20808	148	15	,	,	PUNCT
ajst-20808	148	16	probabilistic	probabilistic	ADJ
ajst-20808	148	17	modeling	modeling	NOUN
ajst-20808	148	18	and	and	CCONJ
ajst-20808	148	19	other	other	ADJ
ajst-20808	148	20	methods	method	NOUN
ajst-20808	148	21	will	will	AUX
ajst-20808	148	22	provide	provide	VERB
ajst-20808	148	23	more	more	ADJ
ajst-20808	148	24	possibilities	possibility	NOUN
ajst-20808	148	25	and	and	CCONJ
ajst-20808	148	26	opportunities	opportunity	NOUN
ajst-20808	148	27	for	for	ADP
ajst-20808	148	28	solving	solve	VERB
ajst-20808	148	29	complex	complex	ADJ
ajst-20808	148	30	problems	problem	NOUN
ajst-20808	148	31	in	in	ADP
ajst-20808	148	32	the	the	DET
ajst-20808	148	33	real	real	ADJ
ajst-20808	148	34	world	world	NOUN
ajst-20808	148	35	.	.	PUNCT
ajst-20808	149	1	references	reference	NOUN
ajst-20808	149	2	[	[	X
ajst-20808	149	3	1	1	NUM
ajst-20808	149	4	]	]	PUNCT
ajst-20808	149	5	y.-t	y.-t	NOUN
ajst-20808	149	6	.	.	PUNCT
ajst-20808	150	1	hsieh	hsieh	PROPN
ajst-20808	150	2	,	,	PUNCT
ajst-20808	150	3	z.	z.	PROPN
ajst-20808	150	4	qi	qi	PROPN
ajst-20808	150	5	,	,	PUNCT
ajst-20808	150	6	and	and	CCONJ
ajst-20808	150	7	d.	d.	PROPN
ajst-20808	150	8	pompili	pompili	PROPN
ajst-20808	150	9	,	,	PUNCT
ajst-20808	150	10	"	"	PUNCT
ajst-20808	150	11	ml	ml	VERB
ajst-20808	150	12	-	-	PUNCT
ajst-20808	150	13	based	base	VERB
ajst-20808	150	14	joint	joint	ADJ
ajst-20808	150	15	doppler	doppler	NOUN
ajst-20808	150	16	estimation	estimation	NOUN
ajst-20808	150	17	and	and	CCONJ
ajst-20808	150	18	compensation	compensation	NOUN
ajst-20808	150	19	in	in	ADP
ajst-20808	150	20	underwater	underwater	ADJ
ajst-20808	150	21	acoustic	acoustic	ADJ
ajst-20808	150	22	communications	communication	NOUN
ajst-20808	150	23	,	,	PUNCT
ajst-20808	150	24	"	"	PUNCT
ajst-20808	150	25	in	in	ADP
ajst-20808	150	26	proceedings	proceeding	NOUN
ajst-20808	150	27	of	of	ADP
ajst-20808	150	28	the	the	DET
ajst-20808	150	29	16th	16th	ADJ
ajst-20808	150	30	international	international	ADJ
ajst-20808	150	31	conference	conference	NOUN
ajst-20808	150	32	on	on	ADP
ajst-20808	150	33	underwater	underwater	ADJ
ajst-20808	150	34	networks	network	NOUN
ajst-20808	150	35	&	&	CCONJ
ajst-20808	150	36	systems	system	NOUN
ajst-20808	150	37	(	(	PUNCT
ajst-20808	150	38	wuwnet	wuwnet	NOUN
ajst-20808	150	39	'	'	NUM
ajst-20808	150	40	22	22	NUM
ajst-20808	150	41	)	)	PUNCT
ajst-20808	150	42	,	,	PUNCT
ajst-20808	150	43	new	new	PROPN
ajst-20808	150	44	york	york	PROPN
ajst-20808	150	45	,	,	PUNCT
ajst-20808	150	46	ny	ny	PROPN
ajst-20808	150	47	,	,	PUNCT
ajst-20808	150	48	usa	usa	PROPN
ajst-20808	150	49	,	,	PUNCT
ajst-20808	150	50	2022	2022	NUM
ajst-20808	150	51	,	,	PUNCT
ajst-20808	150	52	pp	pp	ADV
ajst-20808	150	53	.	.	PUNCT
ajst-20808	151	1	1–8	1–8	X
ajst-20808	151	2	.	.	PUNCT
ajst-20808	152	1	[	[	X
ajst-20808	152	2	2	2	NUM
ajst-20808	152	3	]	]	PUNCT
ajst-20808	152	4	z.	z.	PROPN
ajst-20808	152	5	li	li	PROPN
ajst-20808	152	6	,	,	PUNCT
ajst-20808	152	7	y.	y.	PROPN
ajst-20808	152	8	huang	huang	PROPN
ajst-20808	152	9	,	,	PUNCT
ajst-20808	152	10	m.	m.	PROPN
ajst-20808	152	11	zhu	zhu	PROPN
ajst-20808	152	12	,	,	PUNCT
ajst-20808	152	13	j.	j.	PROPN
ajst-20808	152	14	zhang	zhang	PROPN
ajst-20808	152	15	,	,	PUNCT
ajst-20808	152	16	j.	j.	PROPN
ajst-20808	152	17	chang	chang	PROPN
ajst-20808	152	18	,	,	PUNCT
ajst-20808	152	19	and	and	CCONJ
ajst-20808	152	20	h.	h.	PROPN
ajst-20808	152	21	liu	liu	PROPN
ajst-20808	152	22	,	,	PUNCT
ajst-20808	152	23	“	"	PUNCT
ajst-20808	152	24	feature	feature	NOUN
ajst-20808	152	25	manipulation	manipulation	NOUN
ajst-20808	152	26	for	for	ADP
ajst-20808	152	27	ddpm	ddpm	NOUN
ajst-20808	152	28	based	base	VERB
ajst-20808	152	29	change	change	NOUN
ajst-20808	152	30	detection	detection	NOUN
ajst-20808	152	31	,	,	PUNCT
ajst-20808	152	32	”	"	PUNCT
ajst-20808	152	33	arxiv.org	arxiv.org	PROPN
ajst-20808	152	34	,	,	PUNCT
ajst-20808	152	35	mar	mar	PROPN
ajst-20808	152	36	.	.	PROPN
ajst-20808	152	37	23	23	NUM
ajst-20808	152	38	,	,	PUNCT
ajst-20808	152	39	2024	2024	NUM
ajst-20808	152	40	.	.	PUNCT
ajst-20808	153	1	[	[	X
ajst-20808	153	2	3	3	X
ajst-20808	153	3	]	]	X
ajst-20808	153	4	y.	y.	PROPN
ajst-20808	153	5	lai	lai	PROPN
ajst-20808	153	6	,	,	PUNCT
ajst-20808	153	7	z.	z.	PROPN
ajst-20808	153	8	luo	luo	PROPN
ajst-20808	153	9	,	,	PUNCT
ajst-20808	153	10	and	and	CCONJ
ajst-20808	153	11	z.	z.	PROPN
ajst-20808	153	12	yu	yu	PROPN
ajst-20808	153	13	,	,	PUNCT
ajst-20808	153	14	"	"	PUNCT
ajst-20808	153	15	detect	detect	VERB
ajst-20808	153	16	any	any	DET
ajst-20808	153	17	deepfakes	deepfake	NOUN
ajst-20808	153	18	:	:	PUNCT
ajst-20808	153	19	segment	segment	NOUN
ajst-20808	153	20	anything	anything	PRON
ajst-20808	153	21	meets	meet	VERB
ajst-20808	153	22	face	face	NOUN
ajst-20808	153	23	forgery	forgery	NOUN
ajst-20808	153	24	detection	detection	NOUN
ajst-20808	153	25	and	and	CCONJ
ajst-20808	153	26	localization	localization	NOUN
ajst-20808	153	27	,	,	PUNCT
ajst-20808	153	28	"	"	PUNCT
ajst-20808	153	29	in	in	ADP
ajst-20808	153	30	biometric	biometric	ADJ
ajst-20808	153	31	recognition	recognition	NOUN
ajst-20808	153	32	,	,	PUNCT
ajst-20808	153	33	w.	w.	PROPN
ajst-20808	153	34	jia	jia	PROPN
ajst-20808	153	35	,	,	PUNCT
ajst-20808	153	36	ed	ed	NOUN
ajst-20808	153	37	.	.	PROPN
ajst-20808	153	38	,	,	PUNCT
ajst-20808	153	39	vol	vol	NOUN
ajst-20808	153	40	.	.	PROPN
ajst-20808	153	41	14463	14463	NUM
ajst-20808	153	42	,	,	PUNCT
ajst-20808	153	43	lecture	lecture	NOUN
ajst-20808	153	44	notes	note	NOUN
ajst-20808	153	45	in	in	ADP
ajst-20808	153	46	computer	computer	NOUN
ajst-20808	153	47	science	science	NOUN
ajst-20808	153	48	,	,	PUNCT
ajst-20808	153	49	springer	springer	NOUN
ajst-20808	153	50	,	,	PUNCT
ajst-20808	153	51	singapore	singapore	PROPN
ajst-20808	153	52	,	,	PUNCT
ajst-20808	153	53	2023	2023	NUM
ajst-20808	153	54	.	.	PUNCT
ajst-20808	154	1	[	[	X
ajst-20808	154	2	4	4	X
ajst-20808	154	3	]	]	X
ajst-20808	154	4	q.	q.	PROPN
ajst-20808	154	5	cheng	cheng	PROPN
ajst-20808	154	6	et	et	PROPN
ajst-20808	154	7	al	al	PROPN
ajst-20808	154	8	.	.	PROPN
ajst-20808	154	9	,	,	PUNCT
ajst-20808	154	10	“	"	PUNCT
ajst-20808	154	11	secure	secure	VERB
ajst-20808	154	12	digital	digital	ADJ
ajst-20808	154	13	asset	asset	NOUN
ajst-20808	154	14	transactions	transaction	NOUN
ajst-20808	154	15	:	:	PUNCT
ajst-20808	154	16	integrating	integrate	VERB
ajst-20808	154	17	distributed	distribute	VERB
ajst-20808	154	18	ledger	ledger	NOUN
ajst-20808	154	19	technology	technology	NOUN
ajst-20808	154	20	with	with	ADP
ajst-20808	154	21	safe	safe	ADJ
ajst-20808	154	22	ai	ai	ADJ
ajst-20808	154	23	mechanisms	mechanism	NOUN
ajst-20808	154	24	”	"	PUNCT
ajst-20808	154	25	,	,	PUNCT
ajst-20808	154	26	ajst	ajst	ADJ
ajst-20808	154	27	,	,	PUNCT
ajst-20808	154	28	vol	vol	NOUN
ajst-20808	154	29	.	.	NOUN
ajst-20808	155	1	9	9	NUM
ajst-20808	155	2	,	,	PUNCT
ajst-20808	155	3	no	no	INTJ
ajst-20808	155	4	.	.	NOUN
ajst-20808	155	5	3	3	NUM
ajst-20808	155	6	,	,	PUNCT
ajst-20808	155	7	pp	pp	ADJ
ajst-20808	155	8	.	.	PUNCT
ajst-20808	156	1	156–161	156–161	NUM
ajst-20808	156	2	,	,	PUNCT
ajst-20808	156	3	mar	mar	PROPN
ajst-20808	156	4	.	.	PROPN
ajst-20808	156	5	2024	2024	NUM
ajst-20808	156	6	.	.	PUNCT
ajst-20808	157	1	[	[	X
ajst-20808	157	2	5	5	X
ajst-20808	157	3	]	]	PUNCT
ajst-20808	157	4	s.	s.	PROPN
ajst-20808	157	5	zhao	zhao	PROPN
ajst-20808	157	6	et	et	PROPN
ajst-20808	157	7	al	al	PROPN
ajst-20808	157	8	.	.	PROPN
ajst-20808	157	9	,	,	PUNCT
ajst-20808	157	10	“	"	PUNCT
ajst-20808	157	11	defending	defend	VERB
ajst-20808	157	12	against	against	ADP
ajst-20808	157	13	weight	weight	NOUN
ajst-20808	157	14	-	-	PUNCT
ajst-20808	157	15	poisoning	poison	VERB
ajst-20808	157	16	backdoor	backdoor	NOUN
ajst-20808	157	17	attacks	attack	NOUN
ajst-20808	157	18	for	for	ADP
ajst-20808	157	19	parameter	parameter	NOUN
ajst-20808	157	20	-	-	PUNCT
ajst-20808	157	21	efficient	efficient	ADJ
ajst-20808	157	22	fine	fine	ADJ
ajst-20808	157	23	-	-	PUNCT
ajst-20808	157	24	tuning	tuning	NOUN
ajst-20808	157	25	,	,	PUNCT
ajst-20808	157	26	”	"	PUNCT
ajst-20808	157	27	arxiv.org	arxiv.org	PROPN
ajst-20808	157	28	,	,	PUNCT
ajst-20808	157	29	mar	mar	PROPN
ajst-20808	157	30	.	.	PROPN
ajst-20808	157	31	29	29	NUM
ajst-20808	157	32	,	,	PUNCT
ajst-20808	157	33	2024	2024	NUM
ajst-20808	157	34	.	.	PUNCT
ajst-20808	158	1	[	[	X
ajst-20808	158	2	6	6	NUM
ajst-20808	158	3	]	]	PUNCT
ajst-20808	158	4	j.	j.	PROPN
ajst-20808	158	5	tian	tian	PROPN
ajst-20808	158	6	et	et	PROPN
ajst-20808	158	7	al	al	PROPN
ajst-20808	158	8	.	.	PROPN
ajst-20808	158	9	,	,	PUNCT
ajst-20808	158	10	"	"	PUNCT
ajst-20808	158	11	lesson	lesson	NOUN
ajst-20808	158	12	:	:	PUNCT
ajst-20808	158	13	multi	multi	ADJ
ajst-20808	158	14	-	-	ADJ
ajst-20808	158	15	label	label	ADJ
ajst-20808	158	16	adversarial	adversarial	ADJ
ajst-20808	158	17	false	false	ADJ
ajst-20808	158	18	data	datum	NOUN
ajst-20808	158	19	injection	injection	NOUN
ajst-20808	158	20	attack	attack	NOUN
ajst-20808	158	21	for	for	ADP
ajst-20808	158	22	deep	deep	ADJ
ajst-20808	158	23	learning	learn	VERB
ajst-20808	158	24	locational	locational	ADJ
ajst-20808	158	25	detection	detection	NOUN
ajst-20808	158	26	,	,	PUNCT
ajst-20808	158	27	"	"	PUNCT
ajst-20808	158	28	in	in	ADP
ajst-20808	158	29	ieee	ieee	NOUN
ajst-20808	158	30	transactions	transaction	NOUN
ajst-20808	158	31	on	on	ADP
ajst-20808	158	32	dependable	dependable	ADJ
ajst-20808	158	33	and	and	CCONJ
ajst-20808	158	34	secure	secure	ADJ
ajst-20808	158	35	computing	computing	NOUN
ajst-20808	158	36	.	.	PUNCT
ajst-20808	159	1	[	[	X
ajst-20808	159	2	7	7	X
ajst-20808	159	3	]	]	X
ajst-20808	159	4	xiaotian	xiaotian	PROPN
ajst-20808	159	5	zhang	zhang	PROPN
ajst-20808	159	6	,	,	PUNCT
ajst-20808	159	7	yawen	yawen	PROPN
ajst-20808	159	8	wang	wang	PROPN
ajst-20808	159	9	,	,	PUNCT
ajst-20808	159	10	zhiqing	zhiqing	NOUN
ajst-20808	159	11	xie	xie	PROPN
ajst-20808	159	12	et	et	PROPN
ajst-20808	159	13	al	al	PROPN
ajst-20808	159	14	.	.	PROPN
ajst-20808	159	15	reinforcement	reinforcement	NOUN
ajst-20808	159	16	learning	learning	NOUN
ajst-20808	159	17	approach	approach	NOUN
ajst-20808	159	18	for	for	ADP
ajst-20808	159	19	sequence	sequence	NOUN
ajst-20808	159	20	determination	determination	NOUN
ajst-20808	159	21	of	of	ADP
ajst-20808	159	22	class	class	NOUN
ajst-20808	159	23	integration	integration	NOUN
ajst-20808	159	24	tests	test	VERB
ajst-20808	159	25	[	[	X
ajst-20808	159	26	j	j	X
ajst-20808	159	27	]	]	X
ajst-20808	159	28	.	.	PUNCT
ajst-20808	160	1	computer	computer	NOUN
ajst-20808	160	2	engineering	engineering	NOUN
ajst-20808	160	3	,	,	PUNCT
ajst-20808	160	4	2024	2024	NUM
ajst-20808	160	5	,	,	PUNCT
ajst-20808	160	6	50	50	NUM
ajst-20808	160	7	(	(	PUNCT
ajst-20808	160	8	01	01	NUM
ajst-20808	160	9	):	):	PUNCT
ajst-20808	160	10	68	68	NUM
ajst-20808	160	11	-	-	SYM
ajst-20808	160	12	78	78	NUM
ajst-20808	160	13	.	.	PUNCT
ajst-20808	161	1	[	[	X
ajst-20808	161	2	8	8	NUM
ajst-20808	161	3	]	]	PUNCT
ajst-20808	161	4	rongyun	rongyun	PROPN
ajst-20808	161	5	li	li	PROPN
ajst-20808	161	6	.	.	PROPN
ajst-20808	161	7	reinforcement	reinforcement	NOUN
ajst-20808	161	8	learning	learning	NOUN
ajst-20808	161	9	approach	approach	NOUN
ajst-20808	161	10	for	for	ADP
ajst-20808	161	11	game	game	NOUN
ajst-20808	161	12	manipulation	manipulation	NOUN
ajst-20808	161	13	behavior	behavior	NOUN
ajst-20808	161	14	imitation	imitation	NOUN
ajst-20808	162	1	[	[	X
ajst-20808	162	2	d	d	X
ajst-20808	162	3	]	]	X
ajst-20808	162	4	.	.	PUNCT
ajst-20808	163	1	university	university	NOUN
ajst-20808	163	2	of	of	ADP
ajst-20808	163	3	electronic	electronic	ADJ
ajst-20808	163	4	science	science	NOUN
ajst-20808	163	5	and	and	CCONJ
ajst-20808	163	6	technology	technology	NOUN
ajst-20808	163	7	,	,	PUNCT
ajst-20808	163	8	2022	2022	NUM
ajst-20808	163	9	.	.	PUNCT
ajst-20808	164	1	[	[	X
ajst-20808	164	2	9	9	NUM
ajst-20808	164	3	]	]	X
ajst-20808	164	4	yin	yin	PROPN
ajst-20808	164	5	hang	hang	PROPN
ajst-20808	164	6	.	.	PUNCT
ajst-20808	165	1	epistemic	epistemic	ADJ
ajst-20808	165	2	modeling	modeling	NOUN
ajst-20808	165	3	of	of	ADP
ajst-20808	165	4	self	self	NOUN
ajst-20808	165	5	-	-	PUNCT
ajst-20808	165	6	reinforcement	reinforcement	NOUN
ajst-20808	165	7	learning	learning	NOUN
ajst-20808	165	8	methods	method	NOUN
ajst-20808	165	9	based	base	VERB
ajst-20808	165	10	on	on	ADP
ajst-20808	165	11	deep	deep	ADJ
ajst-20808	165	12	residual	residual	ADJ
ajst-20808	165	13	networks[d	networks[d	NOUN
ajst-20808	165	14	]	]	PUNCT
ajst-20808	165	15	.	.	PUNCT
ajst-20808	166	1	liaoning	liaoning	PROPN
ajst-20808	166	2	university	university	PROPN
ajst-20808	166	3	of	of	ADP
ajst-20808	166	4	engineering	engineering	NOUN
ajst-20808	166	5	and	and	CCONJ
ajst-20808	166	6	technology	technology	NOUN
ajst-20808	166	7	,	,	PUNCT
ajst-20808	166	8	2022	2022	NUM
ajst-20808	166	9	.	.	PUNCT
ajst-20808	167	1	[	[	X
ajst-20808	167	2	10	10	NUM
ajst-20808	167	3	]	]	X
ajst-20808	167	4	wei	wei	PROPN
ajst-20808	167	5	minatong	minatong	PROPN
ajst-20808	167	6	.	.	PUNCT
ajst-20808	168	1	deep	deep	ADJ
ajst-20808	168	2	network	network	NOUN
ajst-20808	168	3	-	-	PUNCT
ajst-20808	168	4	based	base	VERB
ajst-20808	168	5	video	video	NOUN
ajst-20808	168	6	repair	repair	NOUN
ajst-20808	168	7	and	and	CCONJ
ajst-20808	168	8	reinforcement	reinforcement	NOUN
ajst-20808	168	9	learning	learning	NOUN
ajst-20808	168	10	methods	method	NOUN
ajst-20808	168	11	in	in	ADP
ajst-20808	168	12	unstable	unstable	ADJ
ajst-20808	168	13	environments[d	environments[d	PROPN
ajst-20808	168	14	]	]	PUNCT
ajst-20808	168	15	.	.	PUNCT
ajst-20808	169	1	university	university	NOUN
ajst-20808	169	2	of	of	ADP
ajst-20808	169	3	science	science	NOUN
ajst-20808	169	4	and	and	CCONJ
ajst-20808	169	5	technology	technology	NOUN
ajst-20808	169	6	of	of	ADP
ajst-20808	169	7	china	china	PROPN
ajst-20808	169	8	,	,	PUNCT
ajst-20808	169	9	2021	2021	NUM
ajst-20808	169	10	.	.	PUNCT
ajst-20808	170	1	[	[	X
ajst-20808	170	2	11	11	NUM
ajst-20808	170	3	]	]	X
ajst-20808	170	4	zhang	zhang	PROPN
ajst-20808	170	5	wei	wei	PROPN
ajst-20808	170	6	.	.	PUNCT
ajst-20808	171	1	deep	deep	ADJ
ajst-20808	171	2	reinforcement	reinforcement	NOUN
ajst-20808	171	3	learning	learning	NOUN
ajst-20808	171	4	for	for	ADP
ajst-20808	171	5	target	target	NOUN
ajst-20808	171	6	localization	localization	NOUN
ajst-20808	171	7	and	and	CCONJ
ajst-20808	171	8	recognition[d	recognition[d	NOUN
ajst-20808	171	9	]	]	PUNCT
ajst-20808	171	10	.	.	PUNCT
ajst-20808	172	1	xi'an	xi'an	PROPN
ajst-20808	172	2	university	university	PROPN
ajst-20808	172	3	of	of	ADP
ajst-20808	172	4	technology	technology	NOUN
ajst-20808	172	5	,	,	PUNCT
ajst-20808	172	6	2021	2021	NUM
ajst-20808	172	7	.	.	PUNCT
ajst-20808	173	1	[	[	X
ajst-20808	173	2	12	12	NUM
ajst-20808	173	3	]	]	X
ajst-20808	173	4	lei	lei	PROPN
ajst-20808	174	1	xu	xu	PROPN
ajst-20808	174	2	.	.	PUNCT
ajst-20808	175	1	deep	deep	ADJ
ajst-20808	175	2	reinforcement	reinforcement	NOUN
ajst-20808	175	3	learning	learning	NOUN
ajst-20808	175	4	for	for	ADP
ajst-20808	175	5	text	text	NOUN
ajst-20808	175	6	games[d	games[d	NOUN
ajst-20808	175	7	]	]	PUNCT
ajst-20808	175	8	.	.	PUNCT
ajst-20808	176	1	heilongjiang	heilongjiang	PROPN
ajst-20808	176	2	university	university	PROPN
ajst-20808	176	3	,	,	PUNCT
ajst-20808	176	4	2021	2021	NUM
ajst-20808	176	5	.	.	PUNCT