Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

TL;DR

This paper introduces a novel approach to enhance robustness in model-free reinforcement learning by framing it as a multi-objective optimization problem, utilizing Bayesian optimization and control theory metrics, validated on a Furuta pendulum.

Contribution

It proposes a new method combining multi-objective Bayesian optimization with robustness metrics for model-free RL, addressing real-world variability and test-time performance.

Findings

Improved robustness in RL policies demonstrated in hardware experiments.

Effective estimation of robustness metrics from data in a model-free setting.

Balanced performance and robustness achieved in sim-to-real transfer.

Abstract

In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real-world applications, test conditions may differ substantially from the training scenario and, therefore, focusing on pure reward maximization during training may lead to poor results at test time. In these cases, it is important to trade-off between performance and robustness while learning a policy. While several results exist for robust, model-based RL, the model-free case has not been widely investigated. In this paper, we cast the robust, model-free RL problem as a multi-objective optimization problem. To quantify the robustness of a policy, we use delay margin and gain margin, two robustness indicators that are common in control theory. We show how these metrics can be estimated from data in the model-free setting. We use multi-objective Bayesian optimization (MOBO) to solve efficiently this expensive-to-evaluate, multi-objective optimization problem. We show the benefits of our robust formulation both in sim-to-real and pure hardware experiments to balance a Furuta pendulum.