DISTR combines a task-conditioned diffusion model that generates whole high-return trajectories with behavior-cloning replay, and reports higher average success than several baselines on Continual World.
Im- itating human behaviour with diffusion models
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Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay
DISTR combines a task-conditioned diffusion model that generates whole high-return trajectories with behavior-cloning replay, and reports higher average success than several baselines on Continual World.