基于自适应噪声的最大熵进化强化学习方法

王君逸; 王志; 李华雄; 陈春林

doi:10.16383/j.aas.c220103

基于自适应噪声的最大熵进化强化学习方法

doi: 10.16383/j.aas.c220103

1.
南京大学控制科学与智能工程系南京 210008

基金项目: 国家自然科学基金(62006111, 62073160, 62176116), 江苏省自然科学基金(BK20200330)资助

详细信息

作者简介:
王君逸：南京大学控制科学与智能工程系硕士研究生. 2021年获南京大学学士学位. 主要研究方向为强化学习, 机器学习与人工智能. E-mail: mf21150062@smail.nju.edu.cn

王志：南京大学控制科学与智能工程系讲师. 2015年获南京大学学士学位. 2019年获中国香港城市大学博士学位. 主要研究方向为强化学习, 机器学习与人工智能. 本文通信作者. E-mail: zhiwang@nju.edu.cn

李华雄：南京大学控制科学与智能工程系副教授. 2009年获南京大学博士学位. 主要研究方向为机器学习与数据挖掘, 模式识别与智能系统. E-mail: huaxiongli@nju.edu.cn

陈春林：南京大学控制科学与智能工程系教授. 分别于2001年和2006年获中国科学技术大学学士学位和博士学位. 主要研究方向为强化学习及智能无人系统. E-mail: clchen@nju.edu.cn

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出版历程
- 收稿日期: 2022-02-18
- 录用日期: 2022-06-17
- 网络出版日期: 2022-07-22
- 刊出日期: 2023-01-07

Adaptive Noise-based Evolutionary Reinforcement Learning With Maximum Entropy

1.
Department of Control Science and Intelligence Engineering, Nanjing University, Nanjing 210008

Funds: Supported by National Natural Science Foundation of China (62006111, 62073160, 62176116), Natural Science Foundation of Jiangsu Province (BK20200330)

More Information

Author Bio:
WANG Jun-Yi　Master student in the Department of Control Science and Intelligence Engineering, Nanjing University. He received his bachelor degree from Nanjing University in 2021. His research interest covers reinforcement learning, machine learning, and artificial intelligence

WANG Zhi　Lecturer in the Department of Control Science and Intelligence Engineering, Nanjing University. He received his bachelor degree from Nanjing University in 2015. He received his Ph.D. degree from City University of Hong Kong, Ch-ina, in 2019. His research interest covers reinforcement learning, machine learning, and artificial intelligence. Corresponding author of this paper

LI Hua-Xiong　Associate professor in the Department of Control Science and Intelligence Engineering, Nanjing University. He received his Ph.D. degree from Nanjing University in 2009. His research interest covers machine learning, and data mining, pattern recognition, and intelligent systems

CHEN Chun-Lin　Professor in the Department of Control Science and Intelligence Engineering, Nanjing University. He received his bachelor and the Ph.D. degrees from University of Science and Technology of China in 2001 and 2006, respectively. His research interest covers reinforcement learning and intelligent unmanned systems

摘要

摘要: 近年来, 进化策略由于其无梯度优化和高并行化效率等优点, 在深度强化学习领域得到了广泛的应用. 然而, 传统基于进化策略的深度强化学习方法存在着学习速度慢、容易收敛到局部最优和鲁棒性较弱等问题. 为此, 提出了一种基于自适应噪声的最大熵进化强化学习方法. 首先, 引入了一种进化策略的改进办法, 在“优胜”的基础上加强了“劣汰”, 从而提高进化强化学习的收敛速度; 其次, 在目标函数中引入了策略最大熵正则项, 来保证策略的随机性进而鼓励智能体对新策略的探索; 最后, 提出了自适应噪声控制的方式, 根据当前进化情形智能化调整进化策略的搜索范围, 进而减少对先验知识的依赖并提升算法的鲁棒性. 实验结果表明, 该方法较之传统方法在学习速度、最优性收敛和鲁棒性上有比较明显的提升.
- 深度强化学习 /
- 进化策略 /
- 进化强化学习 /
- 最大熵 /
- 自适应噪声
Abstract: Recently, evolution strategies have been widely investigated in the field of deep reinforcement learning due to their promising properties of derivative-free optimization and high parallelization efficiency. However, traditional evolutionary reinforcement learning methods suffer from several problems, including the slow learning speed, the tendency toward local optima, and the poor robustness. A systematic method is proposed, named adaptive noise-based evolutionary reinforcement learning with maximum entropy, to tackle these problems. First, the canonical evolution strategies is introduced to enhance the influence of well-behaved individuals and weaken the impact of those with bad performance, thus improving the learning speed of evolutionary reinforcement learning. Second, a regularization term of maximizing the policy entropy is incorporated into the objective function, which ensures moderate stochastically of actions and encourages the exploration to new promising solutions. Third, the exploration noise is proposed to automatically adapt according to the current evolutionary situation, which reduces the dependence on prior knowledge and promotes the robustness of evolution. Experimental results show that this method achieves faster learning speed, better convergence to global optima, and improved robustness, compared to traditional approaches.
- Deep reinforcement learning /
- evolution strategies /
- evolutionary reinforcement learning /
- maximum entropy /
- adaptive noise

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图 1 基于自适应噪声的最大熵进化强化学习方法的结构

Fig. 1 The structure of AERL-ME

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图 2 实验环境

Fig. 2 Experimental environments

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图 3 对比实验结果

Fig. 3 Comparative experimental results

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图 4 运算时间对比($ n=40$)

Fig. 4 Comparison of operation time ($ n=40$)

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图 5 消融实验结果

Fig. 5 Ablation experimental results

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图 6 初始噪声标准差$ \sigma_0 $和温度因子$ \alpha $的灵敏度分析

Fig. 6 Sensitivity analysis of initial noise standard deviation$ \sigma_0 $and temperature factor$ \alpha $

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表 1 以平均回报表示的数值结果

Table 1 The numerical results in terms of average received returns

环境	AERL-ME	ES	DQN	PPO
CP	500.0 ± 0.0	416.1 ± 54.0	108.3 ± 3.6	460.4 ± 15.8
AB	−83.9 ± 4.5	−173.7 ± 52.9	−98.6 ± 18.5	−77.8 ± 0.5
LL	245.0 ± 11.8	82.8 ± 47.8	35.2 ± 126.9	201.2 ± 10.4
Qbert	677.5 ± 10.3	675.0 ± 34.6	571.0 ± 65.9	540.4 ± 34.0

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