RESEARCH

Dynamics-aware Embeddings

April 25, 2020

Abstract

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and action sequences. These embeddings capture the structure of the environment’s dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.

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AUTHORS

Written by

William F. Whitney

Rajat Agarwal

Kyunghyun Cho

Abhinav Gupta

Publisher

International Conference on Learning Representations (ICLR)

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