From: ICML.cc/2021/Workshop/URL/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposes a method for learning representations without an extrinsic reward which does not assume uniform coverage of the state space. Instead, the representations are constantly refined as the agent explores the environment while the exploration policy is informed by the current representations. The authors demonstrate promising results on both gridworld and continuous control tasks. The question of how exploration affects representation learning and the other way around (and how they should inform one another) could lead to important discussions at this workshop, so we recommend acceptance. 

The paper could be further improved by including comparisons with other approaches for representation learning in RL, exploration, and skill discovery.