An Approximate Bayesian Reinforcement Learning Approach Using Robust Control Policy and Tree Search

Book One

Volume

Proceedings of the International Conference on Automated Planning and Scheduling, 28

Planning and Learning Track

For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL) approach that reduces real-world samples within feasible computational efforts. Firstly, to find an approximate solution of an original undiscounted infinite horizon MB-BRL problem with a cost-free termination, we consider a finite horizon (FH) MB-BRL problem in which terminal costs are given by robust control policies. The resulting performance is better than or equal to the performance obtained with a robust method, while the resulting policy may choose an explorative behavior to get useful information about parametric model uncertainty for reducing real-world samples. Secondly, to obtain a feasible solution of the FH MB-BRL problem using simulation samples, we propose a combination of robust RL, Monte Carlo tree search (MCTS), and Bayesian inference. We show an idea of reusing previous MCTS samples for Bayesian inference at a leaf node. The proposed approach allows an agent to choose from multiple robust policies at a leaf node. Numerical experiments of a two-dimensional peg-in-hole task demonstrate the effectiveness of the proposed approach.

10.1609/icaps.v28i1.13871