Related papers: Exchangeable Input Representations for Reinforcement Learning

Exchangeable Input Representations for Reinforcement Learning

URL: http://arxiv.org/abs/2003.09022v1
Date: Thu, 19 Mar 2020 21:18:55 GMT
Title: Exchangeable Input Representations for Reinforcement Learning
Authors: John Mern and Dorsa Sadigh and Mykel J. Kochenderfer
Abstract summary: This work presents an attention-based method to project neural network inputs into an efficient representation space. We show that our proposed representation results in an input space that is a factor of $m!$ smaller for inputs of $m$ objects.
Score: 48.696389129611056
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an efficient representation space that is invariant under changes to input ordering. We show that our proposed representation results in an input space that is a factor of $m!$ smaller for inputs of $m$ objects. We also show that our method is able to represent inputs over variable numbers of 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 na\"ive approaches.

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