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Offline reinforcement learning (RL) allows for the training of competentagents from offline datasets without any interaction with the environment.Online finetuning of such offline models can further improve performance. Buthow should we ideally finetune agents obtained from offline RL training? Whileoffline RL algorithms can in principle be used for finetuning, in practice,their online performance improves slowly. In contrast, we show that it ispossible to use standard online off-policy algorithms for faster improvement.However, we find this approach may suffer from policy collapse, where thepolicy undergoes severe performance deterioration during initial onlinelearning. We investigate the issue of policy collapse and how it relates todata diversity, algorithm choices and online replay distribution. Based onthese insights, we propose a conservative policy optimization procedure thatcan achieve stable and sample-efficient online learning from offlinepretraining.

Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions