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• Distributional Soft Actor-Critic (DSAC)
• Distributional Soft Actor-Critic with Three Refinements (DSAC-T)

1. Windows 7 or greater or Linux.
2. Python 3.8.
3. The installation path must be in English.

# Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution.
# clone DSAC-T repository
git clone [email protected]/Jingliang-Duan/DSAC-T
cd DSAC-T
# create conda environment
conda env create -f DSAC2.0_environment.yml
conda activate DSAC2.0
# install DSAC2.0
pip install -e.

These are two examples of running DSAC-T on two environments. Train the policy by running:

cd example_train
#Train a pendulum task
python main.py
#Train a humanoid task. To execute this file, Mujoco and Mujoco-py need to be installed first. 
python dsac_mlp_humanoidconti_offserial.py

After training, the results will be stored in the "DSAC-T/results" folder.

In the "main.py/dsac_mlp_humanoidconti_offserial.py" file, you can switch between 'DSAC_V2' and 'DSAC_V1' by changing the "--algorithm" parameter.

In the "DSAC-T/results" folder, pick the path to the folder where the policy will be applied to the simulation and select the appropriate PKL file for the simulation.

python run_policy.py
#you may need to "pip install imageio-ffmpeg" before running this file on Windows. 

After running, the simulation vedio and state&action curve figures will be stored in the "DSAC-T/figures" folder.

We would like to thank all members in Intelligent Driving Laboratory (iDLab), School of Vehicle and Mobility, Tsinghua University for making excellent contributions and providing helpful advices for DSAC-T.