Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

• URL: http://arxiv.org/abs/2210.13846v1
• Date: Tue, 25 Oct 2022 09:08:26 GMT
• Title: Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
• Authors: Yi Zhao, Rinu Boney, Alexander Ilin, Juho Kannala, Joni Pajarinen
• Abstract summary: Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. During online fine-tuning, the performance of the pre-trained agent may collapse quickly due to the sudden distribution shift from offline to online data. We propose to adaptively weigh the behavior cloning loss during online fine-tuning based on the agent's performance and training stability. Experiments show that the proposed method yields state-of-the-art offline-to-online reinforcement learning performance on the popular D4RL benchmark.
• Score: 80.25648265273155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the quality of the offline dataset, such pre-trained agents may have limited performance and would further need to be fine-tuned online by interacting with the environment. During online fine-tuning, the performance of the pre-trained agent may collapse quickly due to the sudden distribution shift from offline to online data. While constraints enforced by offline RL methods such as a behaviour cloning loss prevent this to an extent, these constraints also significantly slow down online fine-tuning by forcing the agent to stay close to the behavior policy. We propose to adaptively weigh the behavior cloning loss during online fine-tuning based on the agent's performance and training stability. Moreover, we use a randomized ensemble of Q functions to further increase the sample efficiency of online fine-tuning by performing a large number of learning updates. Experiments show that the proposed method yields state-of-the-art offline-to-online reinforcement learning performance on the popular D4RL benchmark. Code is available: \url{https://github.com/zhaoyi11/adaptive_bc}.

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