A Scalable Parallel Reinforcement Learning Method Based on Divide-and-Conquer Strategy

To conquer the slow convergence and poor scalability problems of reinforcement learning, a Scalable parallel reinforcement learning method, DCS-SPRL, is proposed on the basis of Divide-and-conquer strategy. In this method, the learning problem with large state space is decomposed into multiple smaller subproblems. According to a weighted priority scheduling algorithm, these subproblems are then dispatched to the learning agents which are able to learn in parallel. Finally, the learning results of each subproblem are merged into a composite solution. The experimental results show that DCS-SPRL has good scalability and needs significantly less computational time.