/deeprl_network

multi-agent deep reinforcement learning for networked system control.

Primary LanguagePython

Networked Multi-agent RL (NMARL)

This repo implements the state-of-the-art MARL algorithms for networked system control, with observability and communication of each agent limited to its neighborhood. For fair comparison, all algorithms are applied to A2C agents, classified into two groups: IA2C contains non-communicative policies which utilize neighborhood information only, whereas MA2C contains communicative policies with certain communication protocols.

Available IA2C algorithms:

Available MA2C algorithms:

Available NMARL scenarios:

  • ATSC Grid: Adaptive traffic signal control in a synthetic traffic grid.
  • ATSC Monaco: Adaptive traffic signal control in a real-world traffic network from Monaco city.
  • CACC Catch-up: Cooperative adaptive cruise control for catching up the leadinig vehicle.
  • CACC Slow-down: Cooperative adaptive cruise control for following the leading vehicle to slow down.

Requirements

Usages

First define all hyperparameters (including algorithm and DNN structure) in a config file under [config_dir] (examples), and create the base directory of each experiement [base_dir]. For ATSC Grid, please call build_file.py to generate SUMO network files before training.

  1. To train a new agent, run
python3 main.py --base-dir [base_dir] train --config-dir [config_dir]

Training config/data and the trained model will be output to [base_dir]/data and [base_dir]/model, respectively.

  1. To access tensorboard during training, run
tensorboard --logdir=[base_dir]/log
  1. To evaluate a trained agent, run
python3 main.py --base-dir [base_dir] evaluate --evaluation-seeds [seeds]

Evaluation data will be output to [base_dir]/eva_data. Make sure evaluation seeds are different from those used in training.

  1. To visualize the agent behavior in ATSC scenarios, run
python3 main.py --base-dir [base_dir] evaluate --evaluation-seeds [seed] --demo

It is recommended to use only one evaluation seed for the demo run. This will launch the SUMO GUI, and view.xml can be applied to visualize queue length and intersectin delay in edge color and thickness.

Reproducibility

The paper results are based on an out-of-date SUMO version 0.32.0. We have re-run the ATSC experiments with SUMO 1.2.0 using the master code, and provided the following training plots as reference. The paper conclusions remain the same.

Grid Monaco

The pytorch impelmention is also avaliable at branch pytorch.

Citation

For more implementation details and underlying reasonings, please check our paper Multi-agent Reinforcement Learning for Networked System Control.

@inproceedings{
chu2020multiagent,
title={Multi-agent Reinforcement Learning for Networked System Control},
author={Tianshu Chu and Sandeep Chinchali and Sachin Katti},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Syx7A3NFvH}
}