GRAM: Generalization in Deep RL with a Robust Adaptation Module
Keywords: deep reinforcement learning, generalization, robust adaptation
TL;DR: We present a framework for generalization in deep reinforcement learning that achieves in-distribution adaptation and out-of-distribution robustness within a single architecture.
Abstract: 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 on a variety of realistic simulated locomotion tasks with a quadruped robot.
Primary Area: reinforcement learning
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Submission Number: 7421
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