安全强化学习综述

王雪松; 王荣荣; 程玉虎

doi:10.16383/j.aas.c220631

安全强化学习综述

doi: 10.16383/j.aas.c220631

1.
中国矿业大学信息与控制工程学院徐州 221116

基金项目: 国家自然科学基金(62176259, 61976215), 江苏省重点研发计划项目(BE2022095)资助

详细信息

作者简介:
王雪松：中国矿业大学教授. 2002年获得中国矿业大学博士学位. 主要研究方向为机器学习, 模式识别. E-mail: wangxuesongcumt@163.com

王荣荣：中国矿业大学博士研究生. 2021年获得济南大学硕士学位. 主要研究方向为深度强化学习. E-mail: wangrongrong1996@126.com

程玉虎：中国矿业大学教授. 2005年获得中国科学院自动化研究所博士学位. 主要研究方向为机器学习, 智能系统. 本文通信作者. E-mail: chengyuhu@163.com

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出版历程
- 收稿日期: 2022-08-08
- 录用日期: 2023-01-11
- 网络出版日期: 2023-03-09
- 刊出日期: 2023-09-26

Safe Reinforcement Learning: A Survey

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116

Funds: Supported by National Natural Science Foundation of China (62176259, 61976215) and Key Research and Development Program of Jiangsu Province (BE2022095)

More Information

Author Bio:
WANG Xue-Song　Professor at China University of Mining and Technology. She received her Ph.D. degree from China University of Mining and Technology in 2002. Her research interest covers machine learning and pattern recognition

WANG Rong-Rong　Ph.D. candidate at China University of Mining and Technology. She received her master degree from University of Jinan in 2021. Her main research interest is deep reinforcement learning

CHENG Yu-Hu　Professor at China University of Mining and Technology. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. His research interest covers machine learning and intelligent system. Corresponding author of this paper

摘要

摘要: 强化学习(Reinforcement learning, RL)在围棋、视频游戏、导航、推荐系统等领域均取得了巨大成功. 然而, 许多强化学习算法仍然无法直接移植到真实物理环境中. 这是因为在模拟场景下智能体能以不断试错的方式与环境进行交互, 从而学习最优策略. 但考虑到安全因素, 很多现实世界的应用则要求限制智能体的随机探索行为. 因此, 安全问题成为强化学习从模拟到现实的一个重要挑战. 近年来, 许多研究致力于开发安全强化学习(Safe reinforcement learning, SRL)算法, 在确保系统性能的同时满足安全约束. 本文对现有的安全强化学习算法进行全面综述, 将其归为三类: 修改学习过程、修改学习目标、离线强化学习, 并介绍了5大基准测试平台: Safety Gym、safe-control-gym、SafeRL-Kit、D4RL、NeoRL. 最后总结了安全强化学习在自动驾驶、机器人控制、工业过程控制、电力系统优化和医疗健康领域中的应用, 并给出结论与展望.
- 安全强化学习 /
- 约束马尔科夫决策过程 /
- 学习过程 /
- 学习目标 /
- 离线强化学习
Abstract: Reinforcement learning (RL) has proved a prominent success in the game of Go, video games, navigation, recommendation systems and other fields. However, a large number of reinforcement learning algorithms cannot be directly transplanted to real physical environment. This is because in the simulation scenario, the agent is able to interact with the environment in a trial-and-error manner to learn the optimal policy. Considering the safety of systems, many real-world applications require the limitation of random exploration behavior of agents. Hence, safety has become an essential factor for reinforcement learning from simulation to reality. In recent years, many researches have been devoted to develope safe reinforcement learning (SRL) algorithms that satisfy safety constraints while ensuring system performance. This paper presents a comprehensive survey of existing SRL algorithms, which are divided into three categories: Modification of learning process, modification of learning objective, and offline reinforcement learning. Furthermore, five experimental platforms are introduced, including Safety Gym, safe-control-gym, SafeRL-Kit, D4RL, and NeoRL. Lastly, the applications of SRL in the fields of autonomous driving, robot control, industrial process control, power system optimization, and healthcare are summarized, and the conclusion and perspective are briefly drawn.
- Safe reinforcement learning (SRL) /
- constrained Markov decision process (CMDP) /
- learning process /
- learning objective /
- offline reinforcement learning

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图 1 安全强化学习方法、基准测试平台与应用

Fig. 1 Methods, benchmarking platforms, and applications of safe reinforcement learning

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表 1 安全强化学习方法对比

Table 1 Comparison of safe reinforcement learning methods

方法类别		训练时安全	部署时安全	与环境实时交互	优点	缺点	应用领域
修改学习过程	环境知识	√	√	√	采样效率高	需获取环境的动力学模型、实现复杂	自动驾驶^{[12−13, 23]}、工业过程控制^[24−25]、电力系统优化^[26]、医疗健康^[21]
	人类知识	√	√	√	加快学习过程	人工监督成本高	机器人控制^{[14, 27]}、电力系统优化^[28]、医疗健康^[29]
	无先验知识	√	√	√	无需获取先验知识、可扩展性强	收敛性差、训练不稳定	自动驾驶^[30]、机器人控制^[31]、工业过程控制^[32]、电力系统优化^[33]、医疗健康^[34]
修改学习目标	拉格朗日法	×	√	√	思路简单、易于实现	拉格朗日乘子选取困难	工业过程控制^[15]、电力系统优化^[16]
修改学习目标	信赖域法	√	√	√	收敛性好、训练稳定	近似误差不可忽略、采样效率低	机器人控制^[35]
离线强化学习	策略约束	√	×	×	收敛性好	方差大、采样效率低	医疗健康^[36]
	值约束	√	×	×	值函数估计方差小	收敛性差	工业过程控制^[22]
	预训练模型	√	×	×	加快学习过程、泛化性强	实现复杂	工业过程控制^[37]

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表 2 安全强化学习基准测试平台对比

Table 2 Comparison of benchmarking platforms for safe reinforcement learning

基准测试平台	任务类型	适用方法	基准算法类型		特点
Safety Gym	机器人导航	修改学习过程与目标	无模型方法	同策略	包含多个高维连续控制任务, 使用最广泛的安全强化学习算法评估平台
safe-control-gym	机器人控制	修改学习过程与目标	无模型方法与基于模型的方法	同策略与异策略	能实现基于模型的方法, 可以方便地与控制类方法进行对比
SafeRL-Kit	自动驾驶	修改学习过程与目标	无模型方法	异策略	首个针对自动驾驶任务的异策略安全强化学习算法基准测试平台
D4RL	机器人导航与控制、自动驾驶	离线强化学习	无模型方法	离线学习	收集有多个环境的离线数据, 已成为离线强化学习算法的标准评估平台
NeoRL	机器人控制、工业控制、股票交易、产品促销	离线强化学习	无模型方法与基于模型的方法	离线学习	包含多个高维或具有高度随机性的现实应用场景任务

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