Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

• URL: http://arxiv.org/abs/2306.04220v6
• Date: Mon, 13 Nov 2023 15:12:45 GMT
• Title: Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
• Authors: Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, Li Jiang
• Abstract summary: offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. We provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets.
• Score: 29.885978495034703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.Code is available at: https://github.com/pcheng2/TSRL

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