关键词: 并行算法, 强化学习, Q-学习, D-S证据理论, 路径规划

Abstract: Reinforcement learning is an important machine learning method.However，slow convergence has been one of main problem in practice.To improve the efficiency of reinforcement learning，this paper proposes parallel reinforcement learning algorithm.There are multiple agents in learning system.In a learning episode，each agent learns independently.After a learning episode，the results of all agents are fused based on D-S evidence theory so as to achieve common result，which are shared by all agents in next learning episode.Experiments show the feasibility and efficiency of the algorithm.

Key words: parallel algorithms, reinforcement learning, Q-learning, D-S evidence theory, path plan