Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
Metadata only
Datum
2020Typ
- Conference Paper
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. © 2020 IEEE. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Buchtitel
2020 IEEE International Conference on Robotics and Automation (ICRA)Seiten / Artikelnummer
Verlag
IEEEKonferenz
Organisationseinheit
03908 - Krause, Andreas / Krause, Andreas
Anmerkungen
Due to the Coronavirus (COVID-19) the conference was conducted virtually.