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arXiv · 2301.12714

Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning

Hanlin Zhu·Paria Rashidinejad·Jiantao Jiao
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Abstract

We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage. Our algorithm combines the marginalized importance sampling framework with the actor-critic paradigm, where the critic returns evaluations of the actor (policy) that are pessimistic relative to the offline data and have a small average (importance-weighted) Bellman error. Compared to existing methods, our algorithm simultaneously offers a number of advantages: (1) It achieves the optimal statistical rate of $1/\sqrt{N}$ -- where $N$ is the size of offline dataset -- in converging to the best policy covered in the offline dataset, even when combined with general function approximators. (2) It relies on a weaker average notion of policy coverage (compared to the $\ell_\infty$ single-policy concentrability) that exploits the structure of policy visitations. (3) It outperforms the data-collection behavior policy over a wide range of specific hyperparameters. We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.

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Hanlin Zhu, Paria Rashidinejad, Jiantao Jiao. 2023-01-30. Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning. https://arxiv.org/abs/2301.12714

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