Dynamic Bottleneck for Robust Self-Supervised ExplorationDownload PDF

May 21, 2021 (edited Oct 25, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Dynamic Bottleneck, Information Bottleneck, Exploration, Reinforcement Learning
  • TL;DR: Robust exploration via dynamic bottleneck-based representation and UCB-based bonus
  • Abstract: Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards. However, such methods are usually sensitive to environmental dynamics-irrelevant information, e.g., white-noise. To handle such dynamics-irrelevant information, we propose a Dynamic Bottleneck (DB) model, which attains a dynamics-relevant representation based on the information-bottleneck principle. Based on the DB model, we further propose DB-bonus, which encourages the agent to explore state-action pairs with high information gain. We establish theoretical connections between the proposed DB-bonus, the upper confidence bound (UCB) for linear case, and the visiting count for tabular case. We evaluate the proposed method on Atari suits with dynamics-irrelevant noises. Our experiments show that exploration with DB bonus outperforms several state-of-the-art exploration methods in noisy environments.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: https://github.com/Baichenjia/DB
17 Replies

Loading