Generating Behaviorally Diverse Policies with Latent Diffusion Models

Shashank Hegde, Sumeet Batra, K.R. Zentner, Gaurav S. Sukhatme

Keywords: Latent Diffusion, Quality Diversity, Reinforcement Learning, Graph Neural Networks

TL;DR: A latent diffusion model that generates a behaviorally diverse set of neural network policies for robotic control

Abstract:

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 humanoid archive 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.

Submission Number: 14894