MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL

Understand

Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).

• However, these works mostly lack the generalization ability across tasks with reward or dynamics change.
• To tackle this challenge, in this paper we propose a task-oriented conditioned diffusion planner for offline meta-RL(MetaDiffuser), which considers the generalization problem as conditional trajectory generation task with contextual representation.
• The key is to learn a context conditioned diffusion model which can generate task-oriented trajectories for planning across diverse tasks.