awesome-diffusion-model-in-rl
This repository compiles research papers that integrate diffusion models with reinforcement learning (RL), regularly updated to reflect the latest in diffusion RL. It highlights advantages such as eliminating bootstrapping dependencies, and mitigating short-sighted behaviors from reward discounting, while harnessing diffusion models' versatility across fields. Suitable for researchers and practitioners focused on diffusion-based policy optimization and planning in offline RL, it also includes papers from leading conferences like ICML, ICLR, and NeurIPS, offering insights into various experimental setups and best practices in RL advancement.
