Learning Representations in Reinforcement Learning: an Information Bottleneck Approach

no code implementations • ICLR 2020 • Yingjun Pei, Xinwen Hou

In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms. We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound.

reinforcement-learning Reinforcement Learning (RL) +1