In this work, we propose Diffusion-QL which utilizes a diffusion model as a highly expressive policy class for behavior cloning and policy regularization. In our approach we learn an action-value function and we add a term maximising action-values to the the training loss of the diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy.
Plese see the requirements.txt file for the detailed python package dependencies for our project.
Running our code is quite easy, such as an example below,
python run_offline.py --env_name walker2d-medium-expert-v2 --algo pcq