jannerm/diffuser

Health v2 · maintenance only

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Adoption not part of the score

1304 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official code for the ICML 2022 paper 'Planning with Diffusion for Flexible Behavior Synthesis', which plans by denoising trajectories with diffusion models. It supports training diffusion models and value-function guided sampling on D4RL locomotion, maze2d, and Kuka block-stacking environments.

Use cases

• train diffusion models for trajectory planning in RL environments
• plan behavior with value-function guided diffusion sampling
• reach goals via inpainting in Maze2D environments
• run block-stacking experiments with a Kuka arm
• sample from pretrained diffusion planners in a Colab notebook
• reproduce results from the Diffuser paper

When to choose

• you want to experiment with diffusion-based planning for control and RL
• you need a reference implementation of the Diffuser paper on D4RL benchmarks
• you want to condition trajectory generation on rewards or goal constraints

When to avoid

• you need a production-ready RL training framework with active support
• you want a general-purpose diffusion model library rather than RL planning code
• you need environments or tooling outside D4RL-style MuJoCo tasks

Facets

library · maturity maintenance

machine-learning deep-learning simulation reinforcement-learning machine-learning artificial-intelligence python diffusion-models reinforcement-learning trajectory-planning d4rl research-code icml-2022 gpu

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