Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization

In offline reinforcement learning (RL), we attempt to learn a control policy from a fixed dataset of environment interactions. This setting has the potential benefit of allowing us to learn effective policies without needing to collect additional interactive data, which can be expensive or dangerous in real-world systems. However, traditional off-policy RL methods tend to perform poorly in this setting due to the distributional shift between the fixed data set and the learned policy. In particular, they tend to extrapolate optimistically and overestimate the action-values outside of the dataset distribution. Recently, two major avenues have been explored to address this issue. First, behavior-regularized methods that penalize actions that deviate from the demonstrated action distribution. Second, uncertainty-aware model-based (MB) methods that discourage state-actions where the dynamics are uncertain. In this work, we propose an algorithmic framework that consists of two stages. In the first stage, we train a policy using behavior-regularized model-free RL on the offline dataset. Then, we can optionally enter a second stage where we fine-tune the policy using our novel Model-Based Behavior-Regularized Policy Optimization (MB2PO) algorithm. We demonstrate that for certain tasks and dataset distributions our conservative model-based fine tuning can greatly increase performance and allow the agent to generalize and outperform the demonstrated behavior. We evaluate our method on a variety of the Gym-MuJoCo tasks in the D4RL benchmark and demonstrate that our method is competitive and in some cases superior to the state of the art for most of the evaluated tasks.