- Windows 7 or greater or Linux.
- Python 3.8.
- 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 DSAC2.0 repository
git clone [email protected]:Jingliang-Duan/Distributional-Soft-Actor-Critic-2.0.git
cd Distributional-Soft-Actor-Critic-2.0
# 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 DSAC2.0 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.pyAfter training, the results will be stored in the "Distributional-Soft-Actor-Critic-2.0/results" folder.
In the "Distributional-Soft-Actor-Critic-2.0/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 "Distributional-Soft-Actor-Critic-2.0/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 2.0.