GRAM: ロバスト適応モジュールによる深層強化学習の汎化
GRAM: Generalization in Deep RL with a Robust Adaptation Module
深層強化学習において、訓練時と同じ環境と未知の環境の両方に適応できるロバスト適応モジュールを提案し、四足歩行ロボットの実験で高い汎化性能を示した。
著者: James Queeney, Xiaoyi Cai, Alexander Schperberg, Radu Corcodel, Mouhacine Benosman, Jonathan P. How
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenarios. In this work, we present a framework for dynamics generalization in deep reinforcement learning that unifies these two distinct types of generalization within a single architecture. We introduce a robust adaptation module that provides a mechanism for identifying and reacting to both in-distribution and out-of-distribution environment dynamics, along with a joint training pipeline that combines the goals of in-distribution adaptation and out-of-distribution robustness. Our algorithm GRAM achieves strong generalization performance across in-distribution and out-of-distribution scenarios upon deployment, which we demonstrate through extensive simulation and hardware locomotion experiments on a quadruped robot.
