Project Overview: Awesome Diffusion Model in RL
Introduction
The "Awesome Diffusion Model in RL" is a curated collection of research papers exploring the use of diffusion models within the realm of Reinforcement Learning (RL). This project aims to present cutting-edge research and developments in what is known as Diffusion Reinforcement Learning (Diffusion RL). It offers a continuously updated resource for academics and practitioners alike who are interested in leveraging the power of diffusion models in RL settings.
Overview of Diffusion Model in RL
The diffusion model in reinforcement learning finds its conceptual underpinnings in the work titled "Planning with Diffusion for Flexible Behavior Synthesis" by Michael Janner and colleagues. The key idea presented is to treat trajectory optimization as a diffusion probabilistic model, enhancing decision-making by incrementally refining trajectories achieved during the learning process.
An alternative approach, "Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning" by Z. Wang, frames the diffusion model as a policy optimization tool in offline RL. This utilizes what is termed as "Diffusion-QL," which considers the policy as a state-conditioned diffusion model.
Advantages
- Avoids Bootstrapping: Eliminates the need for bootstrapping in long-term credit assignment.
- Mitigates Short-sightedness: Addresses issues with short-sighted behaviors resulting from discounted future rewards.
- Versatile Application: Benefiting from the widespread application of diffusion models in language and vision domains, these models are adaptable and scale efficiently across various data modalities.
Paper Collection
The project provides a detailed collection of research papers categorized by publication platforms like Arxiv, ICML, ICLR, CVPR, NeurIPS, and more. Each entry typically includes:
- Title and publication link
- Authors
- Key focus areas or keywords
- Code repositories if available
- Experiment environments mentioned
Example Papers
- Arxiv: Research on topics like "Generative Diffusion Models in Network Optimization" and "3D Diffusion Policy for Visuomotor Policy Learning" provide groundbreaking insights into various applications of diffusion models.
- ICML 2024: Papers like "DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching" are highlighted to show recent advancements.
Contributing
The repository encourages contributions from researchers and developers. By doing so, contributors ensure that the field remains up-to-date with the latest advancements and ideas in diffusion model applications in RL.
License
The project is licensed under the terms specified in its LICENSE file, encouraging academic and research use.
Conclusion
The "Awesome Diffusion Model in RL" project serves as an invaluable resource for those interested in exploring the frontier of reinforcement learning through the lens of diffusion models. It stands as a testament to the ongoing research efforts towards more expressive and effective learning strategies in complex environments.