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arXiv · 1905.02698

Object Exchangeability in Reinforcement Learning: Extended Abstract

John Mern·Dorsa Sadigh·Mykel Kochenderfer
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Abstract

Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge. Careful choice of input representations can help improve efficiency depending on the structure present in the problem. In this work, we present an attention-based method to project inputs into an efficient representation space that is invariant under changes to input ordering. We show that our proposed representation results in a search space that is a factor of m! smaller for inputs of m objects. Our experiments demonstrate improvements in sample efficiency for policy gradient methods on a variety of tasks. We show that our representation allows us to solve problems that are otherwise intractable when using naive approaches.

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John Mern, Dorsa Sadigh, Mykel Kochenderfer. 2019-05-07. Object Exchangeability in Reinforcement Learning: Extended Abstract. https://arxiv.org/abs/1905.02698

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