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arXiv · 1803.00101

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning

Vladimir Feinberg·Alvin Wan·Ion Stoica·Michael I. Jordan·Joseph E. Gonzalez·Sergey Levine
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Abstract

Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of continuous control tasks. Unfortunately, they rely on heuristics that limit usage of the dynamics model. We present model-based value expansion, which controls for uncertainty in the model by only allowing imagination to fixed depth. By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.

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Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine. 2018-02-28. Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. https://arxiv.org/abs/1803.00101

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