AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Lightweight Adaptation in Model-Based Reinforcement Learning
Lisa Torrey

Last modified: 2011-08-24

Abstract


Reinforcement learning algorithms can train an agent to operate successfully in a stationary environment. Most real-world environments, however, are subject to change over time. Research in the areas of transfer learning and lifelong learning addresses this problem by developing new algorithms that allow agents to adapt to environment change. Current trends in this area include model-free learning and data-driven adaptation methods. This paper explores in the opposite direction of those trends. Arguing that model-based algorithms may be better suited to the problem, it looks at adaptation in the context of model-based learning. Noting that standard algorithms themselves have some built-in capability for adaptation, it analyzes when and why a standard algorithm struggles to adapt to environment change. Then it experiments with lightweight and straightforward methods for adapting effectively.

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