https://arxiv.org/abs/2206.13338 (ICUR '22)

Short presentation slides

• MultiAgentCarParkingEnvironment - Unity project containing the implementation of the MDP (environment)
• ppo - scripts related to training PPO and its analysis
• q-learning - scripts related to training Q-Learning and its analysis
• results - results

• MultiAgentCarParkingEnvironment comes with a neural network model attached to the CarAgents. Remove the neural network attached to the Car prefab before building if intending to train (and also set the appropriate initMode of the EnvironmentManager).
• Scripts/Logger in the scene should be enabled if one requires a logger within play mode.

• Unity version 2020.3.14f1
• ML-Agents (https://github.com/Unity-Technologies/ml-agents) - explained in subsequent README files
• Python 3
• C#.NET, Visual Studio or Jetbrains Rider
• CSV file reader e.g. Excel, etc.

Development was done on Windows, with the RL training run on Linux. We have not tested the implementations on other OSs.

Below shows an example trained model with 7 agents and 16 parked cars:

https://youtu.be/xMCpVxDpogA

This work is under the CC BY 4.0 license.

Please cite the arXiv paper if used:

@misc{tanner2022multiagent,
      title={Multi-Agent Car Parking using Reinforcement Learning}, 
      author={Omar Tanner},
      year={2022},
      eprint={2206.13338},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}