强化学习的进化适应批评方法

Xin Xu, Han-gen He, D. Hu
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引用次数: 2

摘要

针对具有连续状态和动作空间的强化学习问题,提出了一种新的混合学习方法。将强化学习问题建模为马尔可夫决策过程(mdp),混合学习方法将进化算法与基于梯度的自适应启发式批评(AHC)算法相结合,以逼近mdp的最优策略。该方法利用进化学习和基于梯度的强化学习的优点来解决强化学习问题。机器人学习控制的仿真结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolutionary adaptive-critic methods for reinforcement learning
In this paper, a novel hybrid learning method is proposed for reinforcement learning problems with continuous state and action spaces. The reinforcement learning problems are modeled as Markov decision processes (MDPs) and the hybrid learning method combines evolutionary algorithms with gradient-based adaptive heuristic critic (AHC) algorithms to approximate the optimal policy of MDPs. The suggested method takes the advantages of evolutionary learning and gradient-based reinforcement learning to solve reinforcement learning problems. Simulation results on the learning control of an acrobot illustrate the efficiency of the presented method.
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