Uncertainty-Driven Pessimistic Q-Ensemble for Offline-to-Online Reinforcement Learning

Re-using existing offline reinforcement learning (RL) agents is an emerging topic for reducing the dominant computational cost for exploration in many settings. To effectively fine-tune the pre-trained offline policies, both offline samples and online interactions may be leveraged. In this paper, we propose the idea of incorporating a pessimistic Q-ensemble and an uncertainty quantification technique to effectively fine-tune offline agents. To stabilize online Q-function estimates during fine-tuning, the proposed method uses uncertainty estimation as a penalization for a replay buffer with a mixture of online interactions from the ensemble agent and offline samples from the behavioral policies. In various robotic tasks on D4RL benchmark, we show that our method outperforms the state-of-the-art algorithms in terms of the average return and the sample efficiency.