multi-commander/Multi-Commander
Multi & Single Agent Reinforcement Learning for Traffic Signal Control Problem
Implements both value-based (DQN, DDQN, Dueling DQN) and policy gradient (PPO, DDPG, TD3, SAC) algorithms alongside distributed methods (IMPALA, A3C, Ape-X) and multi-agent approaches (QMIX, PressLight), all integrated with CityFlow traffic simulator and Ray for distributed training. Built on CityFlow's large-scale urban traffic environment and Ray's distributed computing framework, enabling scalable single and multi-agent RL experiments with TensorFlow backend. Includes Docker containerization with pre-configured dependencies and visualization ports for rapid deployment.
130 stars. No commits in the last 6 months.
Stars
130
Forks
30
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 28, 2022
Commits (30d)
0
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