Diffusion Self-Weighted Guidance for Offline Reinforcement Learning
This work addresses a computational bottleneck in offline RL for AI agents, offering an incremental improvement in efficiency.
The paper tackles the challenge of computing scores in offline reinforcement learning with diffusion models by introducing a diffusion over actions and weights, enabling direct score extraction without extra networks. The proposed Self-Weighted Guidance method performs on par with state-of-the-art methods on D4RL environments while simplifying training.
Offline reinforcement learning (RL) recovers the optimal policy $π$ given historical observations of an agent. In practice, $π$ is modeled as a weighted version of the agent's behavior policy $μ$, using a weight function $w$ working as a critic of the agent's behavior. Though recent approaches to offline RL based on diffusion models have exhibited promising results, the computation of the required scores is challenging due to their dependence on the unknown $w$. In this work, we alleviate this issue by constructing a diffusion over both the actions and the weights. With the proposed setting, the required scores are directly obtained from the diffusion model without learning extra networks. Our main conceptual contribution is a novel guidance method, where guidance (which is a function of $w$) comes from the same diffusion model, therefore, our proposal is termed Self-Weighted Guidance (SWG). We show that SWG generates samples from the desired distribution on toy examples and performs on par with state-of-the-art methods on D4RL's challenging environments, while maintaining a streamlined training pipeline. We further validate SWG through ablation studies on weight formulations and scalability.