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Graph Neural Network Reinforcement Learning for AMoD Systems

Official implementation of Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems


Prerequisites

You will need to have a working IBM CPLEX installation. If you are a student or academic, IBM is releasing CPLEX Optimization Studio for free. You can find more info here

To install all required dependencies, run

pip install -r requirements.txt

Contents

  • src/algos/a2c_gnn.py: PyTorch implementation of A2C-GNN.
  • src/algos/reb_flow_solver.py: thin wrapper around CPLEX formulation of the Rebalancing problem (Section III-A in the paper).
  • src/envs/amod_env.py: AMoD simulator.
  • src/cplex_mod/: CPLEX formulation of Rebalancing and Matching problems.
  • src/misc/: helper functions.
  • data/: json files for NYC experiments.
  • saved_files/: directory for saving results, logging, etc.

Examples

To train an agent, main.py accepts the following arguments:

cplex arguments:
    --cplexpath     defines directory of the CPLEX installation
    
model arguments:
    --test          activates agent evaluation mode (default: False)
    --max_episodes  number of episodes to train agent (default: 16k)
    --max_steps     number of steps per episode (default: T=60)
    --no-cuda       disables CUDA training (default: True, i.e. run on CPU)
    --directory     defines directory where to log files (default: saved_files)
    
simulator arguments: (unless necessary, we recommend using the provided ones)
    --seed          random seed (default: 10)
    --demand_ratio  (default: 0.5)
    --json_hr       (default: 7)
    --json_tsetp    (default: 3)
    --no-beta       (default: 0.5)

Important: Take care of specifying the correct path for your local CPLEX installation. Typical default paths based on different operating systems could be the following

Windows: "C:/Program Files/ibm/ILOG/CPLEX_Studio128/opl/bin/x64_win64/"
OSX: "/Applications/CPLEX_Studio128/opl/bin/x86-64_osx/"
Linux: "/opt/ibm/ILOG/CPLEX_Studio128/opl/bin/x86-64_linux/"

Training and simulating an agent

  1. To train an agent (with the default parameters) run the following:
python main.py
  1. To evaluate a pretrained agent run the following:
python main.py --test=True

Credits

This work was conducted as a joint effort with Kaidi Yang*, James Harrison*, Filipe Rodrigues', Francisco C. Pereira' and Marco Pavone*, at Technical University of Denmark' and Stanford University*.

Reference

@inproceedings{GammelliYangEtAl2021,
  author = {Gammelli, D. and Yang, K. and Harrison, J. and Rodrigues, F. and Pereira, F. C. and Pavone, M.},
  title = {Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems},
  year = {2021},
  note = {Submitted},
}

In case of any questions, bugs, suggestions or improvements, please feel free to contact me at [email protected].