GitHub / SECNetLabUNM / HTransRL
Hybrid Transformer based Multi-agent Reinforcement Learning (HTransRL) is for drone coordination in air corridors, addressing the challenges of dynamic dimensions and types of state inputs, which cannot addressed by the traditional MARL.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SECNetLabUNM%2FHTransRL
Stars: 3
Forks: 1
Open issues: 0
License: None
Language: Python
Size: 97 MB
Dependencies parsed at: Pending
Created at: 12 months ago
Updated at: 11 months ago
Pushed at: 12 months ago
Last synced at: 8 months ago
Topics: advanced-air-mobility, air-corridor, multiagent-reinforcement-learning, proximal-policy-optimization, reinforcement-learning, transformer, unmanned-aerial-vehicle, urban-air-mobility