Spotlight

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Yue Wu · Shuangfei Zhai · Nitish Srivastava · Joshua M Susskind · Jian Zhang · Ruslan Salakhutdinov · Hanlin Goh

[ Abstract ] [ Livestream: Visit Reinforcement Learning 7 ] [ Paper ]
Tue 20 Jul 6:20 p.m. — 6:25 p.m. PDT

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.

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