Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding

This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed MATS-LP approach utilizes a combination of Monte Carlo Tree Search and reinforcement learning for resolving conflicts.

Paper: Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding

Installation:

To run MATS-LP, your system needs C++ Build Tools (CMake, g++, build-essential), python3 and ONNX runtime.

Installation of Python packages:

pip3 install -r docker/requirements.txt

Installation of ONNX runtime:

wget https://github.com/microsoft/onnxruntime/releases/download/v1.14.1/onnxruntime-linux-x64-1.14.1.tgz \
    && tar -xf onnxruntime-linux-x64-1.14.1.tgz \
    && cp onnxruntime-linux-x64-1.14.1/lib/* /usr/lib/ && cp onnxruntime-linux-x64-1.14.1/include/* /usr/include/

Optionally, you could use the Dockerfile to build the image:

cd docker && sh build.sh

Alternatively, to pull the pre-built image from Docker Hub and tag it for local use, execute:

docker pull tviskaron/mats-lp && docker tag tviskaron/mats-lp mats-lp

Running MATS-LP:

To execute the MATS-LP algorithm and produce an animation using pre-trained weights of CostTracer, use the following command:

python3 main.py

You can adjust the environment and algorithm's parameters using arguments. For example:

python3 main.py --map_name wfi_warehouse --num_agents 10
python3 main.py --map_name pico_s24_od30_na32 --num_agents 10 --seed 42
python3 main.py --map_name mazes-s0_wc8_od55 --num_expansions 500 --num_threads 8

The animation will be stored in the renders folder. The default parameters of MATS-LP are set to the values used in the paper.

Using docker:

docker run --rm -ti -u $(id -u):$(id -g) -v $(pwd):/code -w /code mats-lp python3 main.py

We offer a Google Colab example that simplifies the process:

Raw Data:

The raw data, comprising the results of our experiments for MATS-LP, can be downloaded from the following link: Download Raw Data

Citation:

If you find this code helpful in your research, please cite our paper as follows:

@article{skrynnik2023decentralized,
  title={Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding},
  author={Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr},
  journal={arXiv preprint arXiv:2312.15908},
  year={2023}
}