Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change

TL;DR

This paper addresses the challenge of enabling reinforcement learning agents to adapt efficiently to sudden environmental changes by combining prioritized exploration with structured knowledge preservation.

Contribution

It introduces a novel framework that integrates prioritized exploration and structured representations for rapid adaptation in non-stationary environments.

Findings

Enhanced adaptation speed in changing environments

Reduced catastrophic forgetting during updates

Improved policy robustness in dynamic settings

Abstract

Real-world autonomous decision-making systems, from robots to recommendation engines, must operate in environments that change over time. While deep reinforcement learning (RL) has shown an impressive ability to learn optimal policies in stationary environments, most methods are data intensive and assume a world that does not change between training and test time. As a result, conventional RL methods struggle to adapt when conditions change. This poses a fundamental challenge: how can RL agents efficiently adapt their behavior when encountering novel environmental changes during deployment without catastrophically forgetting useful prior knowledge? This dissertation demonstrates that efficient online adaptation requires two key capabilities: (1) prioritized exploration and sampling strategies that help identify and learn from relevant experiences, and (2) selective preservation of prior knowledge through structured representations that can be updated without disruption to reusable components.