Multi-threading parallel reinforcement learning
by Qiming Fu; Yiyi Kang; Zhen Gao; Hongjie Wu; Fuyuan Hu; Jianping Chen; Shan Zhong
International Journal of Computer Applications in Technology (IJCAT), Vol. 61, No. 4, 2019

Abstract: With respect to the problem of the slow convergence of the traditional reinforcement learning algorithm in practical applications, we propose a novel multi-threading parallel reinforcement learning algorithm - MPRL. MPRL is mainly composed of two parts. One is the FCM-based reinforcement learning multi-threading partitioning method, which transforms the multi-threading partitioning problem into a clustering partition problem to obtain the optimal multi-threading partitioning solution. The other is the parallel reinforcement learning framework, which makes the parallel execution between the policy evaluation and the interaction with the environment. In the learning process, the experience replay is adopted to update the value function, which can also solve the problem of the non-convergence in the off-policy evaluation. Experimentally, the MPRL algorithm is applied to the windy grid world problem and the cart pole problem, and compared with Q-Learning, Sarsa and KCACL. The experimental results show that MPRL has a faster convergence rate and better convergence performance.

Online publication date: Fri, 25-Oct-2019

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