多目的ベイズ最適化によるロバストなモデルフリー強化学習
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
制御理論の遅延余裕とゲイン余裕をロバスト性指標としてデータから推定し、多目的ベイズ最適化で性能とロバスト性のトレードオフを取るモデルフリー強化学習手法を提案し、Furuta振り子のsim2realと実機実験で有効性を示した。
著者: Matteo Turchetta, Andreas Krause, Sebastian Trimpe
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
