/MAPF_G2RL

Implementation of the G2RL approach in the POGEMA environment

Primary LanguageJupyter NotebookMIT LicenseMIT

Multi-Agent Pathfinding in POGEMA with G2RL

Implementation of the G2RL [1] approach in the POGEMA environment.

Basic Concepts

Problem: MAPF
Environment: 2D grid with static obstacles and dynamic agents
Agent actions: wait, up, down, left, right
Local observations: free cells, static obstacles, dynamic agents
Global guidance: the shortest traversable path considering all static obstacles
Objective: minimize the overall number of steps and avoid conflicts

Code Implementation

Partially based on the repo.
Installation:

pip install -r requirements.txt
pip install .

Train & Test

Notebook with simple examples of training and testing implementations.

Demonstration

Demo

References

[1] B. Wang, Z. Liu, Q. Li, and A. Prorok, "Mobile robot path planning in dynamic environments through globally guided reinforcement learning," IEEE Robot. Autom. Lett., vol. 5, no. 4, pp. 6932–6939, Oct. 2020.