README.md

Multi-Commander

Multi-agent signal control

Usage

Single agent for single intersection

Training

DQN

python run_rl_control.py --algo DQN --epoch 200 --num_step 2000 --phase_step 1

Double DQN

python run_rl_control.py --algo DDQN --epoch 200 --num_step 2000 --phase_step 1

Dueling DQN

python run_rl_control.py --algo DuelDQN --epoch 200 --num_step 2000 --phase_step 1

Inference

DQN

python run_rl_control.py --algo DQN --inference --num_step 3000 --ckpt model/DQN_20190803_150924/DQN-200.h5

DDQN

python run_rl_control.py --algo DDQN --inference --num_step 2000 --ckpt model/DDQN_20190801_085209/DDQN-100.h5

Dueling DQN

python run_rl_control.py --algo DuelDQN --inference --num_step 2000 --ckpt model/DuelDQN_20190730_165409/DuelDQN-ckpt-10

Simulation

. simulation.sh

open firefox with the url: http://localhost:8080/?roadnetFile=roadnet.json&logFile=replay.txt

Multiple intersections signal control

Training

QMIX (based on Ray)

python ray_multi_agent.py

MDQN

python run_rl_multi_control.py --algo MDQN --epoch 1000 --num_step 500 --phase_step 10

Inference

MDQN

python run_rl_multi_control.py --algo MDQN --inference --num_step 1500 --phase_step 15 --ckpt model/XXXXXXX/MDQN-1.h5

Rule based

1*6 roadnet

Generate checkpoint

python run_rl_multi_control.py --algo MDQN --epoch 1 --num_step 1 --phase_step 15

Generate replay file

python run_rl_multi_control.py --algo MDQN --inference --num_step 1500 --phase_step 15 --ckpt model/XXXXXXX/MDQN-1.h5

Simulation

. simulation.sh

open firefox with the url: http://localhost:8080/?roadnetFile=roadnet.json&logFile=replay.txt

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