Deep-Multi-agent-Reinforcement-Learning-for-Highway-On-Ramp-Merging

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Deep-Multi-agent-Reinforcement-Learning-for-Highway-On-Ramp-Merging

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# Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

This is the repository for implementation of "Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic" by Dong Chen and Zhaojian Li and Yongqiang Wang and Longsheng Jiang and Yue Wang. An on-policy MARL algorithm for highway on-ramp merging problem, which features parameter sharing, action masking, local reward design and a priority-based safety supervisor. The goal was to replicate the results obtained by the authors of the paper.

## Algorithms

All the MARL algorithms are extended from the single-agent RL with parameter sharing among agents.
- [ ] MAA2C: TBD.
- [x] MAPPO.
- [x] MAACKTR.
- [x] MADQN: Does not work well.
- [ ] MASAC: TBD.

## Installation
- create an python virtual environment: `conda create -n marl_cav python=3.6 -y`
- active the virtul environment: `conda activate marl_cav`
- install pytorch (torch>=1.2.0): `pip install torch===1.7.0 torchvision===0.8.1 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html`
- install the requirements: `pip install -r requirements.txt`

<img src="docs/on-ramp.png" alt="output_example" width="50%" height="50%">
 Fig.1 Illustration of the considered on-ramp merging traffic scenario. CAVs (blue) and HDVs (green) coexist on both ramp and through lanes.

## Demo
[see the site](https://drive.google.com/drive/u/1/folders/1437My4sDoyPFsUjrThmlu1oJjTkTkvJ7)

## Usage
To run the code, just run it via `python run_xxx.py`.  The config files contain the parameters for the MARL policies.

## Training curves

 <img src="docs/plot_benchmark_safety1.png" alt="output_example" width="90%" height="90%">
 Fig.2 Performance comparison between the proposed method and 3 state-of-the-art MARL algorithms.

To reproduce, we train the algorithms for 3 random seeds, 0, 2000, 2021. For example, we can set the *torch_seed* and *seed* to 0
to run the seed 0. We can plot the comparison curves with the code: `python common/plot_benchmark_safety.py`

## Cite
```
@misc{chen2021deep,
      title={Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic}, 
      author={Dong Chen and Zhaojian Li and Yongqiang Wang and Longsheng Jiang and Yue Wang},
      year={2021},
      eprint={2105.05701},
      archivePrefix={arXiv},
      primaryClass={eess.SY}
}
```

## Reference
- [Highway-env](https://github.com/eleurent/highway-env)

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