# MultiVehicleEnv

**Repository Path**: ShelyH/multi-vehicle-env

## Basic Information

- **Project Name**: MultiVehicleEnv
- **Description**: MultiVehicleEnv
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-09-17
- **Last Updated**: 2023-09-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MultiVehicleEnv
This is a simulator designed for MARL(Multi-Agent Reinforcement Learning) Algorithm researchers to train robot motion control strategies.

- The simulator models robots with Ackerman, Mecanum and differential steering and lidar, which can be applied to common tasks such as obstacle avoidance, navigation, environment exploration,etc.

- We realize the method proposed in our paper to accelerate robot kinematics simulation and lidar rendering, achieving about **2.56** and **14.2** times simulation speedup respectively. 

This repository contains the code for the following papers:
- [Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning](https://ieeexplore.ieee.org/document/9812263).

![Demo](docs/record.gif)

## Code Structure
```plaintext
.
├─docs/
├─src/
│  ├─MultiVehicleEnv/
│  │  ├─scenarios/
│  │  ├─basic.py
│  │  ├─environment.py
│  │  ├─evaluate.py
│  │  ├─geometry.py
│  │  ├─GUI.py
│  │  ├─rendering.py
│  │  ├─scenario.py
│  │  └─utils.py
│  └─setup.py
└─README.md
```
- The main part of simulator is implemented under the folder `src/MultiVehicleEnv` , including the attribute definitions and physical modeling of `Vehicle`, `Obstacle` and `Lidar`, as well as API for the whole system.

- In `src/MultiVehicleEnv/scenarios`, we combine the above elements to form complete scenarios. Under the folder `src/MultiVehicleEnv`, for each scenario we have defined, we train the RL strategy to accomplish a specific task.




## Install
### Prerequisites
Python (3.8.10), OpenAI gym (0.18.3), pyglet(1.5.15), numpy (1.20.3)
### Instructions
Just install this repo by:
```shell
git clone https://gitee.com/ShelyH/multi-vehicle-env.git
cd MultiVehicleEnv/src
pip install -e .
```

## Getting Started
The API of simulator is Gym-like, which is friendly and easy to understand for researchers.

You can take `src/MultiVehicleEnv/test_multi_car.py` as an example to get started. In this script, we instantiate a scenario for cooperative navigation (defined in `src/MultiVehicleEnv/scenarios/multi_reach_road.py`) and obtain the simulation environment to  train and evaluate RL strategy.