Showing your offline reinforcement learning work: Online evaluation budget matters


Over the recent years, vast progress has been made in Offline Reinforcement Learning (Offline-RL) for various decision-making domains: from finance to robotics. However, comparing and reporting new Offline-RL algorithms has been noted as underdeveloped: (1) use of unlimited online evaluation budget for hyperparameter search (2) sidestepping offline policy selection (3) ad-hoc performance statistics reporting. In this work, we propose an evaluation technique addressing these issues, Expected Online Performance, that provides a performance estimate for a best-found policy given a fixed online evaluation budget. Using our approach, we can estimate the number of online evaluations required to surpass a given behavioral policy performance. Applying it to several Offline-RL baselines, we find that with a limited online evaluation budget, (1) Behavioral Cloning constitutes a strong baseline over various expert levels and data regimes, and (2) offline uniform policy selection is competitive with value-based approaches. We hope the proposed technique will make it into the toolsets of Offline-RL practitioners to help them arrive at informed conclusions when deploying RL in real-world systems.

Offline Reinforcement Learning Workshop at Neural Information Processing Systems, 2021
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