Reinforcement Learning with Side Information for the Uncertainties

Abstract

Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the presence of uncertainties. While our previous study introduced SMC to the reinforcement learning (RL) based on approximate dynamic programming in the context of optimal control, SMC is introduced to a conventional RL framework in this work. As a specific realization, the modified twin delayed deep deterministic policy gradient (DDPG) for consensus was exploited to develop sliding mode RL. Numerical experiments show that the sliding mode RL outperforms existing state-of-the-art RL methods and model-based methods in terms of the mean square error (MSE) performance.