Evolutionary Computation for Reinforcement Learning
Evolutionary Computation for Reinforcement Learning
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DOI：
10.1007/978-3-642-27645-3_10
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Shimon Whiteson
中科院分区：
文献类型：
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作者：
Shimon Whiteson
进化计算算法模拟自然选择过程来解决优化问题，是发现高性能强化学习策略的有效工具。因为进化强化学习方法可以自动找到良好的表示、处理连续的动作空间并应对部分可观察性，所以它们具有很强的经验记录，有时显着优于时间差异方法。本章概述了进化计算在强化学习中的应用研究，概述了进化神经网络拓扑和权重的方法，也使用时间差分方法的混合方法，多智能体设置的协同进化方法，生成和发展系统，以及在线进化强化学习方法。
Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces, and cope with partial observability, evolutionary reinforcement-learning approaches have a strong empirical track record, sometimes significantly outperforming temporal-difference methods. This chapter surveys research on the application of evolutionary computation to reinforcement learning, overviewing methods for evolving neural-network topologies and weights, hybrid methods that also use temporal-difference methods, coevolutionary methods for multi-agent settings, generative and developmental systems, and methods for on-line evolutionary reinforcement learning.