  Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has
enabled learning a collection of behaviorally diverse, high performing
policies. However, these methods typically involve storing thousands of
policies, which results in high space-complexity and poor scaling to additional
behaviors. Condensing the archive into a single model while retaining the
performance and coverage of the original collection of policies has proved
challenging. In this work, we propose using diffusion models to distill the
archive into a single generative model over policy parameters. We show that our
method achieves a compression ratio of 13x while recovering 98% of the original
rewards and 89% of the original coverage. Further, the conditioning mechanism
of diffusion models allows for flexibly selecting and sequencing behaviors,
including using language. Project website:
https://sites.google.com/view/policydiffusion/home
