Actor-critic algorithm with incremental dual natural policy gradient - Peng ZHANG; Quan LIU; Shan ZHONG; Jian-wei ZHAI; Wei-sheng QIAN (Tongxin xuebao)
The existed algorithms for continuous action space failed to consider the way of selecting optimal action and utilizing the knowledge of the action space,so an efficient actor-critic algorithm was proposed by improving the natural gradient.The objective of the proposed algorithm was to maximize the expected return.Upper and the lower bounds of the action range were weighted to obtain the optimal action.The two bounds were approximated by linear function.Afterward,the problem of obtaining the optimal action was transferred to the learning of double policy parameter vectors.To speed the learning,the incremental Fisher information matrix and the eligibilities of both bounds were designed.At three reinforcement learning problems,compared with other representative methods with continuous action space,the simulation results show that the proposed algorithm has the advantages of rapid convergence rate and high convergence stability.