BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning

Understand

Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets.

• However, such static datasets often exhibit distribution bias, resulting in limited generalizability.
• To address this limitation, a straightforward solution is data augmentation (DA), which leverages generative models to enrich data distribution.
• Despite the promising results, current DA techniques focus solely on reconstructing future trajectories from given states, while ignoring the exploration of history transitions that reach them.