让强化学习智能体在环境突变时快速适应，同时不丢失已有知识。
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change
- 通过优先探索和采样策略聚焦关键经验
- 用结构化表示选择性保留旧知识
- 适合需要实时适应的机器人与推荐系统
现实世界的自主决策系统（如机器人、推荐引擎）必须在动态环境中运行。尽管深度强化学习（RL）在静态环境下表现出色，但多数方法依赖大量数据且假设训练与测试环境一致。当部署中环境发生突变时，传统RL方法难以适应，且易导致灾难性遗忘。本论文提出高效在线适应需具备两项能力：(1) 优先探索与采样策略，以识别并学习相关经验；(2) 通过结构化表示选择性保留先验知识，实现更新而不破坏可复用模块。该方法显著提升智能体在突发环境变化下的适应效率与稳定性。
原文摘要 · Abstract (English)
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.
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