diffusers/examples/reinforcement_learning

Diffusion-based Policy Learning for RL

diffusion_policy implements Diffusion Policy, a diffusion model that predicts robot action sequences in reinforcement learning tasks.

This example implements a robot control model for pushing a T-shaped block into a target area. The model takes in current state observations as input, and outputs a trajectory of subsequent steps to follow.

To execute the script, run diffusion_policy.py

Diffuser Locomotion

These examples show how to run Diffuser in Diffusers. There are two ways to use the script, run_diffuser_locomotion.py.

The key option is a change of the variable n_guide_steps. When n_guide_steps=0, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment. By default, n_guide_steps=2 to match the original implementation.

You will need some RL specific requirements to run the examples:

pip install -f https://download.pytorch.org/whl/torch_stable.html \
                free-mujoco-py \
                einops \
                gym==0.24.1 \
                protobuf==3.20.1 \
                git+https://github.com/rail-berkeley/d4rl.git \
                mediapy \
                Pillow==9.0.0