Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning

arXiv:2404.09521v1 Announce Type: new
Abstract: In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training. We argue that understanding and utilizing contextual cues, such as the gravity level of the environment, is critical for robust generalization, and we propose to integrate the learning of context representations directly with policy learning. Our algorithm demonstrates improved generalization on various simulated domains, outperforming prior context-learning techniques in …

arxiv behavior context cs.ai cs.lg reinforcement reinforcement learning type zero-shot