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Inheriting the design philosophy of CleanRL and Diffusers, CleanDiffuser emphasizes usability, simplicity, and customizability. We hope that CleanDiffuser will serve as a foundational tool library, providing long-term support for Diffusion Model research in the decision-making community, facilitating the application of research for scientists. # Reinforcement Learning $ python pipelines/diffuser_d4rl_mujoco.py # Imitation Learning (need to download the dataset, see below) $ python pipelines/dp_pusht.py.
Leveraging the powerful generative capability of diffusion models (DMs) to build decision-making agents has achieved extensive success. However, there is still a demand for an easy-to-use and modularized open-source library that offers customized and efficient development for DM-based decision-making algorithms. In this work, we introduce CleanDiffuser, the first DM library specifically.
There aren't any releases here You can create a release to package software, along with release notes and links to binary files, for other people to use. about releases in our docs. Figure 5: Diffuser Implementation with CleanDiffuser.
The left part is a minimal code example showcasing simplicity and readability, and the right part provides a code explanation where the algorithm implementation can be entirely represented as a combination of building blocks, showing an example of various pipelines. This site uses Just the Docs, a documentation theme for Jekyll. Template Repository CleanDiffuser GitHub Documentation ArXiv.
CleanDiffuserTeam has 2 repositories available. Follow their code on GitHub. Getting Started The cleandiffuser folder contains the core components of the CleanDiffuser codebase, including Diffusion Models, Network Architectures, and Guided Sampling.
It also provides unified Env and Dataset Interfaces. In the tutorials folder, we provide the simplest runnable tutorials and algorithms, which can be understood in conjunction with the documentation. In CleanDiffuser, we.