Distributed Heuristic Multi-Agent Path Finding with Communication

06/21/2021
by   Ziyuan Ma, et al.
0

Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn cooperation to handle congested situations. This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution. To guide RL algorithm on long-horizon goal-oriented tasks, we embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works. Our method treats each agent independently and trains the model from a single agent's perspective. The final trained policy is applied to each agent for decentralized execution. The whole system is distributed during training and is trained under a curriculum learning strategy. Empirical evaluation in obstacle-rich environment indicates the high success rate with low average step of our method.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/12/2021

Learning Selective Communication for Multi-Agent Path Finding

Learning communication via deep reinforcement learning (RL) or imitation...
research
07/05/2023

SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is a crucial component for many large-sc...
research
11/04/2020

Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together

Multi-agent path finding in formation has many potential real-world appl...
research
07/30/2020

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

Multi-agent navigation in dynamic environments is of great industrial va...
research
02/08/2022

Multi-Agent Path Finding with Prioritized Communication Learning

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale...
research
03/03/2021

Inference-Based Deterministic Messaging For Multi-Agent Communication

Communication is essential for coordination among humans and animals. Th...
research
10/17/2019

MAPEL: Multi-Agent Pursuer-Evader Learning using Situation Report

In this paper, we consider a territory guarding game involving pursuers,...

Please sign up or login with your details

Forgot password? Click here to reset