Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
This addresses the need for robust policies in real-world RL applications where test conditions vary, though it is incremental as it extends existing robust RL methods to the model-free case.
The paper tackles the problem of poor test-time performance in reinforcement learning when training conditions differ from real-world scenarios by proposing a robust model-free RL approach that trades off performance and robustness using multi-objective Bayesian optimization. The result is demonstrated in sim-to-real and hardware experiments to balance a Furuta pendulum, showing benefits in robustness metrics like delay and gain margins.
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.