Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change
This addresses a fundamental challenge for real-world autonomous systems like robots and recommendation engines, but appears incremental as it builds on existing RL methods.
The paper tackles the problem of reinforcement learning agents struggling to adapt to sudden environmental changes during deployment, and demonstrates that efficient online adaptation requires prioritized exploration and selective preservation of prior knowledge.
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.