Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions

• URL: http://arxiv.org/abs/2302.12003v1
• Date: Sun, 12 Feb 2023 13:27:34 GMT
• Title: Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
• Authors: Qiyuan Liu, Qi Zhou, Rui Yang, Jie Wang
• Abstract summary: Clustering with Bisimulation Metrics (CBM) learns robust representations by grouping visual observations in the latent space. CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes; (2) learning a set of prototypes according to the current cluster assignments. Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms.
• Score: 9.088460902782547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint.To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes; (2) learning a set of prototypes according to the current cluster assignments. Computing cluster assignments with bisimulation metrics enables CBM to capture task-relevant information, as bisimulation metrics quantify the behavioral similarity between observations. Moreover, CBM encourages the consistency of representations within each group, which facilitates filtering out task-irrelevant information and thus induces robust representations against distractions. An appealing feature is that CBM can achieve sample-efficient representation learning even if multiple distractions exist simultaneously.Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms and achieves state-of-the-art performance on both multiple and single distraction settings. The code is available at https://github.com/MIRALab-USTC/RL-CBM.

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