强化学习中的策略重用: 研究进展

doi:10.12305/j.issn.1001-506X.2022.03.21

摘要/Abstract

摘要：

策略重用(policy reuse, PR)作为一种迁移学习(transfer learning, TL)方法, 通过利用任务之间的内在联系, 将过去学习到的经验、知识用于加速学习当前的目标任务, 不仅能够在很大程度上解决传统强化学习(reinforcement learning, RL)收敛速度慢、资源消耗大等问题, 而且避免了在相似问题上难以复用的问题。本文综述了RL中的PR方法, 将现有方法细分为策略重构、奖励设计、问题转换、相似性度量等方面来分别介绍和分析各自的特点, 及其在多智能体场景和深度RL(deep RL, DRL)中的扩展。并且, 介绍了源和目标任务之间的映射方法。最后, 基于当前PR的应用, 叙述了该课题在未来发展方向上的一些猜想和假设。

关键词: 强化学习, 迁移学习, 策略重用, 任务映射

Abstract:

Policy reuse (PR) is a transfer learning (TL) method. By using the internal connection among tasks, the experience and knowledge learned in the past can be used to accelerate the learning of the current target task. To a large extent, it solves the problems of traditional reinforcement learning (RL), such as slow convergence speed and high resource consumption, and avoids the problem of difficult reuse on similar problems. This paper reviews PR methods in RL, subdivided as policy reconstruction, reward shaping, problem transformation and similarity measurement, presents their characteristics respectively, and introduces their extensions in multi-agent scenarios and deep RL (DRL). Then, the mapping methods between source and target tasks are introduced. Finally, based on the current application of PR, some conjectures and assumptions about the future development direction of this subject are described.

Key words: reinforcement learning (RL), transfer learning (TL), policy reuse (PR), task mapping

中图分类号:

TP181

何立, 沈亮, 李辉, 王壮, 唐文泉. 强化学习中的策略重用: 研究进展[J]. 系统工程与电子技术, 2022, 44(3): 884-899.

Li HE, Liang SHEN, Hui LI, Zhuang WANG, Wenquan TANG. Survey on policy reuse in reinforcement learning[J]. Systems Engineering and Electronics, 2022, 44(3): 884-899.

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文献	RL框架	MDP区别	映射函数
[19]	Sarsa	S, A	Q_S→Q_T
[20]	Q-learning	A, R	Q_S→Advice
[22]	Sarsa(λ)	S, R	M(s_A)→r′
[28]	无限制	S_S×A_S≠S_T×A_T	$ \mathcal{T}_{S} \rightarrow \mathcal{T}_{T}$
[23]	值迭代	S	S_S→S_T

文献	算法名称	方法类别	完成工作
[34]	具有后继特征的广义策略改进	外部协助(专家策略)	在拥有不同奖励函数的源任务上训练专家策略, 并用神经网络评估专家策略, 选择Q值最大的专家策略来重用
[36]	AC框架中的专家演示	外部协助(离线专家演示)	使用预训练后的值函数加速基于AC框架的算法如DDPG
[39]	深度Q学习	外部协助(在线专家演示)	抛弃预训练的过程, 将专家演示直接用于RL阶段进行训练
[41]	基于DDPG框架和HEB的专家演示	外部协助(离线与在线结合的专家演示)	结合离线预训练和在线学习的方法来使用专家演示, 使用DDPG框架学习
[33, 45]	PRQL	概率探索	在重用策略库的过程中同时增加选择随机的动作的概率以避免收敛到次优策略
[48]	DQL+OFU	概率探索	结合DQL算法和定向探索策略OFU学习目标任务
[54]	ILPSS	策略蒸馏/整合	构建出用神经网络拟合概率模型的采样轨迹去学习目标策略的增量框架
[55]	策略蒸馏	策略蒸馏/整合	将多个任务最优策略蒸馏整合到单个策略后重用

文献	算法	MDP的区别	奖励设计函数的形式	算法分析
[60]	PBRS	M_s=M_t	F=γΦ(s′)－Φ(s)	用定义在状态空间上的势函数的差值作为附加奖励值, 并能保证最优策略的不变性
[61]	PBA	M_s=M_t	F=γΦ(s′, a′)－Φ(s, a)	在保证最优策略不变性的前提下, 将势函数扩展到基于状态-动作对上, 能用更加具体的信息去指导智能体选择动作
[62]	DPB	M_s=M_t	F=γΦ(s′, t′)－Φ(s, t)	将状态与时间结合创建动态的势函数, 除了能保证最优策略的不变性之外, 在多智能体环境中纳什均衡解也不会改变
[63]	DPBA	M_s=M_t	F_t=γΦ_t+1(s′, a′)－Φ_t(s, a)	能将任何先验知识作为额外奖励加入动态势函数, 提高了算法的通用性
[64]	PTS	S_s≠S_t, A_s≠A_t	F_t=γΦ_t+1(s′, a′)－Φ_t(s, a)	将源策略编码成基于动态势函数的奖励设计函数, 对次优的源策略具有更强的鲁棒性
[68]	LIRPG	M_s=M_t	$F_{t}=\gamma\left(r_{t}^{e x}+\lambda r_{\eta}^{i n}\left(s_{t}, a_{t}\right)\right) $	利用外在奖励优化内在奖励并使用内外奖励的和去更新策略, 适用于大部分的RL算法

文献	完成工作	优点	缺点
[75-77]	交互模拟形式量化MDP之间的差异	量化了MDP之间的差异	需要手动定义任务之间的度量, 只适用于离散状态空间, 计算量大
[19]	半自动方法, 手动定义源和目标任务之间的关系	度量源域和目标域之间的相似性更为准确	半自动, 需要人类定义源域和目标域之间的关系, 通用性差
[81]	从智能体与环境交互收集的样本中估计任务相似性	全自动, 智能体自主学习度量相似性并进行PR	容易导致经验过拟合
[82]	用自模拟度量构造不稳定环境下MDP分布之间的距离, 并以此结合小公式集来迁移策略	提供了PR在不稳定环境下的解决思路	不适用庞大的状态空间和连续状态空间问题
[83]	Hausdorff度量方法计算有限个MDP之间的距离, Kantorovich度量方法计算概率分布之间的距离	提供了度量不同任务状态集和概率分布之间的方法	存在错误度量的问题, 不适用于连续状态空间问题
[85]	使用KL散度衡量策略之间的差异来筛选策略, 并完成策略库的重建	避免重用源策略的无用部分而导致的负迁移问题, 保证了策略库的健壮性和有效性	不适用于连续动作空间问题
[48]	Jensen-Shannon度量方法计算概率分布之间的距离	最大限度地重用了源策略, 并通过限制目标任务中的探索提高了重用效率	只适用于离散的状态空间和动作空间问题

方法	优点	缺点
策略重构类	简单, 短时间内达到较好的效果	难以保证源策略完全适用于目标任务, 导致效果次优
奖励设计类	克服稀疏奖励, 只需细微改动RL框架	奖励函数设计困难, 收敛较慢
问题转换类	简化问题, 利用已有问题模型解决	现有问题模型较少, 约束条件苛刻
相似性度量类	直观选择最优的源策略	相似性度量标准不统一, 通用性较差