The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Visando a contradição entre exploração e exploração na aprendizagem por reforço profundo, este artigo propõe “estratégia de exploração baseada em recompensa combinada com seleção de ação Softmax” (RBE-Softmax) como uma estratégia de exploração dinâmica para guiar o agente a aprender. A superioridade do método proposto é que a característica do processo de aprendizagem do agente é utilizada para adaptar os parâmetros de exploração online, e o agente é capaz de selecionar a ação ideal potencial de forma mais eficaz. O método proposto é avaliado em tarefas de controle discretas e contínuas no OpenAI Gym, e os resultados da avaliação empírica mostram que o método RBE-Softmax leva a uma melhoria estatisticamente significativa no desempenho de algoritmos de aprendizagem por reforço profundo.
Zhi-xiong XU
Army Engineering University
Lei CAO
Army Engineering University
Xi-liang CHEN
Army Engineering University
Chen-xi LI
Army Engineering University
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Zhi-xiong XU, Lei CAO, Xi-liang CHEN, Chen-xi LI, "Reward-Based Exploration: Adaptive Control for Deep Reinforcement Learning" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2409-2412, September 2018, doi: 10.1587/transinf.2018EDL8011.
Abstract: Aiming at the contradiction between exploration and exploitation in deep reinforcement learning, this paper proposes “reward-based exploration strategy combined with Softmax action selection” (RBE-Softmax) as a dynamic exploration strategy to guide the agent to learn. The superiority of the proposed method is that the characteristic of agent's learning process is utilized to adapt exploration parameters online, and the agent is able to select potential optimal action more effectively. The proposed method is evaluated in discrete and continuous control tasks on OpenAI Gym, and the empirical evaluation results show that RBE-Softmax method leads to statistically-significant improvement in the performance of deep reinforcement learning algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8011/_p
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@ARTICLE{e101-d_9_2409,
author={Zhi-xiong XU, Lei CAO, Xi-liang CHEN, Chen-xi LI, },
journal={IEICE TRANSACTIONS on Information},
title={Reward-Based Exploration: Adaptive Control for Deep Reinforcement Learning},
year={2018},
volume={E101-D},
number={9},
pages={2409-2412},
abstract={Aiming at the contradiction between exploration and exploitation in deep reinforcement learning, this paper proposes “reward-based exploration strategy combined with Softmax action selection” (RBE-Softmax) as a dynamic exploration strategy to guide the agent to learn. The superiority of the proposed method is that the characteristic of agent's learning process is utilized to adapt exploration parameters online, and the agent is able to select potential optimal action more effectively. The proposed method is evaluated in discrete and continuous control tasks on OpenAI Gym, and the empirical evaluation results show that RBE-Softmax method leads to statistically-significant improvement in the performance of deep reinforcement learning algorithms.},
keywords={},
doi={10.1587/transinf.2018EDL8011},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Reward-Based Exploration: Adaptive Control for Deep Reinforcement Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2409
EP - 2412
AU - Zhi-xiong XU
AU - Lei CAO
AU - Xi-liang CHEN
AU - Chen-xi LI
PY - 2018
DO - 10.1587/transinf.2018EDL8011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - Aiming at the contradiction between exploration and exploitation in deep reinforcement learning, this paper proposes “reward-based exploration strategy combined with Softmax action selection” (RBE-Softmax) as a dynamic exploration strategy to guide the agent to learn. The superiority of the proposed method is that the characteristic of agent's learning process is utilized to adapt exploration parameters online, and the agent is able to select potential optimal action more effectively. The proposed method is evaluated in discrete and continuous control tasks on OpenAI Gym, and the empirical evaluation results show that RBE-Softmax method leads to statistically-significant improvement in the performance of deep reinforcement learning algorithms.
ER -