Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning

• URL: http://arxiv.org/abs/2403.05385v5
• Date: Thu, 1 Aug 2024 16:02:06 GMT
• Title: Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
• Authors: Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári,
• Abstract summary: We show that the number of samples needed to learn a near-optimal policy with FQI-log scales with the accumulated cost of the optimal policy. We empirically verify that FQI-log uses fewer samples than FQI trained with squared loss on problems where the optimal policy reliably achieves the goal.
• Score: 57.154674117714265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with FQI-log scales with the accumulated cost of the optimal policy, which is zero in problems where acting optimally achieves the goal and incurs no cost. In doing so, we provide a general framework for proving small-cost bounds, i.e. bounds that scale with the optimal achievable cost, in batch RL. Moreover, we empirically verify that FQI-log uses fewer samples than FQI trained with squared loss on problems where the optimal policy reliably achieves the goal.

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