Soft Switching Expert Policies for Controlling Systems with Uncertain Parameters

This paper proposes a simulation-based reinforcement learning algorithm for controlling systems with uncertain and varying system parameters. While simulators are useful for safely learning control policies for physical systems, mitigating the reality gap remains a major challenge. To address the challenge, we propose a two-stage algorithm. In the first stage, multiple control policies are learned for systems with different parameters in a simulator. In the second stage, for a real system, the control policies learned in the first stage are smoothly switched using an online convex optimization algorithm based on observations. Our proposed algorithm is demonstrated through numerical experiments.