Reference

• Distributional Soft Actor-Critic (DSAC)

Requires

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

Installation

# Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution.
# clone DSAC_v1 repository
git clone git@github.com/Jingliang-Duan/DSAC_v1
cd DSAC_v1
# create conda environment
conda env create -f DSAC1.0_environment.yml
conda activate DSAC1.0
# install DSAC1.0
pip install -e.

Train

These are two examples of running DSAC-v1 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_v1/results" folder.

Simulation

In the "DSAC-v1/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_v1/figures" folder.

Acknowledgment

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-v1.