The problem of how to balance the exploration and exploitation in the large or continuous state space is a hot topic in the field of reinforcement learning. With respect to this problem,this paper presented a novel actor-critic algorithm which combined with function approximation method and Gaussian process method. In the terms of actor,the algorithm used the temporal difference error to construct a mean square error function with respect to the policy parameters. In the terms of critic,the algorithm used G...
长春130012)0引言强化学习(reinforcement learning,RL)即模拟动物自主学习理论,具有模型无关性和在线学习性两个特征。在强化学习中agent不需要环境模型和先验知识,通过试错(trial and er-ror)的方式与环境交互来获得知识并进行自主动作选择,具有自主学习能力。在每一轮交互中
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