  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.
