Cognitive-AI-Systems/when-to-switch

"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️

When to Switch: Planning and Learning For Partially Observable Multi-Agent Pathfinding

This repository provides the implementation of the "When to Switch" paper, offering various policies and algorithms designed to address the challenging problem of finding non-conflicting paths for a set of agents in an environment that is only partially observable to each agent (PO-MAPF). The repository includes two main policies: one is based on search-based re-planning (RePlan), and the other is based on reinforcement learning (EPOM). Additionally, the repository features three implementations of mixed policies, which switch between RePlan and EPOM.

Installation

Install all dependencies using:

pip install -r docker/requirements.txt

Inference Example

To download pretrained weights, use this link 137MB

Execute EPOM, RePlan, ASwitcher, LSwitcher, and HSwitcher to generate animations using pre-trained weights with the following command:

python example.py

The animations will be stored in the renders folder.

Training EPOM

To train EPOM, execute train_epom.py with the learning/train.yaml config file:

python train_epom.py --config_path="learning/train.yaml"

Training LSwitcher

To train LSwitcher estimator for the RePlan or EPOM algorithm, use the commands below:

python train_lswitcher.py --algo="RePlan"

python train_lswitcher.py --algo="EPOM"

Citation

If you use this repository in your research or wish to reference it, please cite our TNNLS paper:

@article{skrynnik2023switch,
    title = {When to Switch: Planning and Learning for Partially Observable Multi-Agent Pathfinding},
    author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr I},
    journal = {IEEE Transactions on Neural Networks and Learning Systems},
    year = {2023},
    publisher = {IEEE}
}