Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning

A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal...
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Autor*in:	Dongfang Zhao [verfasserIn] Jiafeng Liu [verfasserIn] Rui Wu [verfasserIn] Dansong Cheng [verfasserIn] Xianglong Tang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Reinforcement learning information entropy optimistic sampling data efficiency

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 55763-55769
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:55763-55769

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2913001

Katalog-ID:	DOAJ047339632

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520			\|a A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency.
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spelling	10.1109/ACCESS.2019.2913001 doi (DE-627)DOAJ047339632 (DE-599)DOAJ48d31e9b72f04b8d9172e2a4e4bab208 DE-627 ger DE-627 rakwb eng TK1-9971 Dongfang Zhao verfasserin aut Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency. Reinforcement learning information entropy optimistic sampling data efficiency Electrical engineering. Electronics. Nuclear engineering Jiafeng Liu verfasserin aut Rui Wu verfasserin aut Dansong Cheng verfasserin aut Xianglong Tang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 55763-55769 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:55763-55769 https://doi.org/10.1109/ACCESS.2019.2913001 kostenfrei https://doaj.org/article/48d31e9b72f04b8d9172e2a4e4bab208 kostenfrei https://ieeexplore.ieee.org/document/8698221/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 55763-55769
allfields_unstemmed	10.1109/ACCESS.2019.2913001 doi (DE-627)DOAJ047339632 (DE-599)DOAJ48d31e9b72f04b8d9172e2a4e4bab208 DE-627 ger DE-627 rakwb eng TK1-9971 Dongfang Zhao verfasserin aut Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency. Reinforcement learning information entropy optimistic sampling data efficiency Electrical engineering. Electronics. Nuclear engineering Jiafeng Liu verfasserin aut Rui Wu verfasserin aut Dansong Cheng verfasserin aut Xianglong Tang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 55763-55769 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:55763-55769 https://doi.org/10.1109/ACCESS.2019.2913001 kostenfrei https://doaj.org/article/48d31e9b72f04b8d9172e2a4e4bab208 kostenfrei https://ieeexplore.ieee.org/document/8698221/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 55763-55769
allfieldsGer	10.1109/ACCESS.2019.2913001 doi (DE-627)DOAJ047339632 (DE-599)DOAJ48d31e9b72f04b8d9172e2a4e4bab208 DE-627 ger DE-627 rakwb eng TK1-9971 Dongfang Zhao verfasserin aut Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency. Reinforcement learning information entropy optimistic sampling data efficiency Electrical engineering. Electronics. Nuclear engineering Jiafeng Liu verfasserin aut Rui Wu verfasserin aut Dansong Cheng verfasserin aut Xianglong Tang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 55763-55769 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:55763-55769 https://doi.org/10.1109/ACCESS.2019.2913001 kostenfrei https://doaj.org/article/48d31e9b72f04b8d9172e2a4e4bab208 kostenfrei https://ieeexplore.ieee.org/document/8698221/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 55763-55769
allfieldsSound	10.1109/ACCESS.2019.2913001 doi (DE-627)DOAJ047339632 (DE-599)DOAJ48d31e9b72f04b8d9172e2a4e4bab208 DE-627 ger DE-627 rakwb eng TK1-9971 Dongfang Zhao verfasserin aut Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency. Reinforcement learning information entropy optimistic sampling data efficiency Electrical engineering. Electronics. Nuclear engineering Jiafeng Liu verfasserin aut Rui Wu verfasserin aut Dansong Cheng verfasserin aut Xianglong Tang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 55763-55769 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:55763-55769 https://doi.org/10.1109/ACCESS.2019.2913001 kostenfrei https://doaj.org/article/48d31e9b72f04b8d9172e2a4e4bab208 kostenfrei https://ieeexplore.ieee.org/document/8698221/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 55763-55769
language	English
source	In IEEE Access 7(2019), Seite 55763-55769 volume:7 year:2019 pages:55763-55769
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topic_facet	Reinforcement learning information entropy optimistic sampling data efficiency Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Dongfang Zhao @@aut@@ Jiafeng Liu @@aut@@ Rui Wu @@aut@@ Dansong Cheng @@aut@@ Xianglong Tang @@aut@@
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callnumber-first	T - Technology
author	Dongfang Zhao
spellingShingle	Dongfang Zhao misc TK1-9971 misc Reinforcement learning misc information entropy misc optimistic sampling misc data efficiency misc Electrical engineering. Electronics. Nuclear engineering Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
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topic_title	TK1-9971 Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning Reinforcement learning information entropy optimistic sampling data efficiency
topic	misc TK1-9971 misc Reinforcement learning misc information entropy misc optimistic sampling misc data efficiency misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Reinforcement learning misc information entropy misc optimistic sampling misc data efficiency misc Electrical engineering. Electronics. Nuclear engineering
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title	Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
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title_auth	Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
abstract	A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency.
abstractGer	A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency.
abstract_unstemmed	A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency.
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title_short	Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
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