Model-based Trajectory Stitching for Improved Offline Reinforcement Learning

Understand

In many real-world applications, collecting large and high-quality datasets may be too costly or impractical.

• Offline reinforcement learning (RL) aims to infer an optimal decision-making policy from a fixed set of data.
• Getting the most information from historical data is then vital for good performance once the policy is deployed.
• We propose a model-based data augmentation strategy, Trajectory Stitching (TS), to improve the quality of sub-optimal historical trajectories.