This is the code for implementing the SCRIMP algorithm :SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding
Python == 3.7
pip install -r requirements.txt
- cd into the od_mstar3 folder.
- python3 setup.py build_ext --inplace.
- Check by going back to the root of the git folder, running python3 and
import od_mstar3.cpp_mstar.
- Modify the parameters in
alg_parameters.pyto set the desired training setting and recording methods. - Call python
driver.py.
alg_parameters.py - Training parameters.
driver.py - Driver of program. Holds global training network for PPO.
episodic_buffer.py - Defines the episodic buffer used to generate intrinsic rewards.
eval_model.py - Evaluates trained model.
mapf_gym.py - Defines the classical Reinforcement Learning environment of Multi-Agent Pathfinding.
model.py - Defines the neural network-based operation model.
net.py - Defines network architecture.
runner.py - A single process for collecting training data.
Fully trained SCRIMP model - https://www.dropbox.com/scl/fo/ekhxyt7gm575kfwaerwb5/h?rlkey=j3cdikwofz0zelj2oci9q97k8&dl=0
Yutong Wang
Bairan Xiang
Shinan Huang
Guillaume Sartoretti