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arXiv · 1812.01129

Mitigating Planner Overfitting in Model-Based Reinforcement Learning

Dilip Arumugam·David Abel·Kavosh Asadi·Nakul Gopalan·Christopher Grimm·Jun Ki Lee·Lucas Lehnert·Michael L. Littman
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Abstract

An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model. Alternatively, it can take a more conservative stance and eschew its model in favor of optimizing its behavior solely via real-world interaction. This latter approach can be exceedingly slow to learn from experience, while the former can lead to "planner overfitting" - aspects of the agent's behavior are optimized to exploit errors in its model. This paper explores an intermediate position in which the planner seeks to avoid overfitting through a kind of regularization of the plans it considers. We present three different approaches that demonstrably mitigate planner overfitting in reinforcement-learning environments.

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Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman. 2018-12-03. Mitigating Planner Overfitting in Model-Based Reinforcement Learning. https://arxiv.org/abs/1812.01129

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