Dynamic Bottleneck for Robust Self-Supervised Exploration

• URL: http://arxiv.org/abs/2110.10735v1
• Date: Wed, 20 Oct 2021 19:17:05 GMT
• Title: Dynamic Bottleneck for Robust Self-Supervised Exploration
• Authors: Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang
• Abstract summary: 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. Our experiments show that exploration with DB bonus outperforms several state-of-the-art exploration methods in noisy environments.
• Score: 84.78836146128236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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