Addressing Distribution Shift in Online Reinforcement Learning with Offline DatasetsDownload PDF

28 Sept 2020 (modified: 05 May 2023)ICLR 2021 Conference Blind SubmissionReaders: Everyone
Keywords: reinforcement learning, offline reinforcement learning, control, distribution shift
Abstract: Recent progress in offline reinforcement learning (RL) has made it possible to train strong RL agents from previously-collected, static datasets. However, depending on the quality of the trained agents and the application being considered, it is often desirable to improve such offline RL agents with further online interaction. As it turns out, fine-tuning offline RL agents is a non-trivial challenge, due to distribution shift – the agent encounters out-of-distribution samples during online interaction, which may cause bootstrapping error in Q-learning and instability during fine-tuning. In order to address the issue, we present a simple yet effective framework, which incorporates a balanced replay scheme and an ensemble distillation scheme. First, we propose to keep separate offline and online replay buffers, and carefully balance the number of samples from each buffer during updates. By utilizing samples from a wider distribution, i.e., both online and offline samples, we stabilize the Q-learning. Next, we present an ensemble distillation scheme, where we train an ensemble of independent actor-critic agents, then distill the policies into a single policy. In turn, we improve the policy using the Q-ensemble during fine-tuning, which allows the policy updates to be more robust to error in each individual Q-function. We demonstrate the superiority of our method on MuJoCo datasets from the recently proposed D4RL benchmark suite.
One-sentence Summary: We present a simple framework, BRED, that incorporates a balanced replay scheme and an ensemble distillation scheme, for fine-tuning an offline RL agent more efficiently.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Reviewed Version (pdf): https://openreview.net/references/pdf?id=iqiI9ebYFh
16 Replies

Loading