GitHub / GameDisplayer / Deep-QLearning-Multi-Agent-Perspective-for-Traffic-Signal-Control
Experiments in which Deep Reinforcement Learning agents try to choose the correct traffic light phase at an intersection to maximize the traffic efficiency. (Deep Q-Learning and Independent Deep Q-Networks)
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PURL: pkg:github/GameDisplayer/Deep-QLearning-Multi-Agent-Perspective-for-Traffic-Signal-Control
Fork of AndreaVidali/Deep-QLearning-Agent-for-Traffic-Signal-Control
Stars: 1
Forks: 1
Open issues: 3
License: mit
Language: Python
Size: 111 MB
Dependencies parsed at: Pending
Created at: over 4 years ago
Updated at: over 1 year ago
Pushed at: almost 4 years ago
Last synced at: over 1 year ago
Commit Stats
Commits: 152
Authors: 5
Mean commits per author: 30.4
Development Distribution Score: 0.434
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/GameDisplayer/Deep-QLearning-Multi-Agent-Perspective-for-Traffic-Signal-Control
Topics: deep-reinforcement-learning, simulation, sumo, tensorflow-gpu, traffic-control