  Offline Reinforcement Learning promises to learn effective policies from
previously-collected, static datasets without the need for exploration.
However, existing Q-learning and actor-critic based off-policy RL algorithms
fail when bootstrapping from out-of-distribution (OOD) actions or states. We
hypothesize that a key missing ingredient from the existing methods is a proper
treatment of uncertainty in the offline setting. We propose Uncertainty
Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs
and down-weights their contribution in the training objectives accordingly.
Implementation-wise, we adopt a practical and effective dropout-based
uncertainty estimation method that introduces very little overhead over
existing RL algorithms. Empirically, we observe that UWAC substantially
improves model stability during training. In addition, UWAC out-performs
existing offline RL methods on a variety of competitive tasks, and achieves
significant performance gains over the state-of-the-art baseline on datasets
with sparse demonstrations collected from human experts.
