| --- |
| license: mit |
| tags: |
| - multi-agent-path-finding |
| - mapf |
| - multi-agent-reinforcement-learning |
| - reinforcement-learning |
| - robotics |
| - decentralized-planning |
| - pytorch |
| --- |
| |
| # SYLPH: Social Behavior as a Key to Learning-Based Multi-Agent Pathfinding Dilemmas |
|
|
| This repository provides the pretrained model for **SYLPH**, introduced in: |
|
|
| > **Social Behavior as a Key to Learning-Based Multi-Agent Pathfinding Dilemmas** |
|
|
| SYLPH is a learning-based Multi-Agent Path Finding (MAPF) framework that explicitly models social behaviors among agents to improve decentralized coordination in challenging multi-agent interactions. |
|
|
| The pretrained checkpoint provided here corresponds to the policy used for evaluation in the paper. |
|
|
| ## Model Overview |
|
|
| Multi-Agent Path Finding requires multiple agents to navigate toward their individual goals while avoiding collisions with obstacles and other agents. |
|
|
| Learning-based decentralized MAPF methods can struggle in challenging interaction scenarios, particularly when agents encounter coordination dilemmas caused by competing paths and limited shared space. |
|
|
| SYLPH introduces social behavior into the learned policy to facilitate coordination among agents and improve their ability to resolve these interactions. |
|
|
| ### Key Features |
|
|
| - Decentralized multi-agent path planning |
| - Learning-based coordination |
| - Socially-aware agent behavior |
| - Multi-agent reinforcement learning |
| - Designed for challenging MAPF interaction scenarios |
| - Shared policy across agents |
|
|
| ## Model Files |
|
|
| The repository contains the pretrained SYLPH policy checkpoint: |
|
|
| ```text |
| net_checkpoint.pkl |
| |
