  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
