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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.

Demo

Code Structure

.
├─docs/
├─src/
│  ├─MultiVehicleEnv/
│  │  ├─scenarios/
│  │  ├─basic.py
│  │  ├─environment.py
│  │  ├─evaluate.py
│  │  ├─geometry.py
│  │  ├─GUI.py
│  │  ├─rendering.py
│  │  ├─scenario.py
│  │  └─utils.py
│  └─setup.py
├─test/
└─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/test, 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:

git clone https://github.com/efc-robot/MultiVehicleEnv.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/test_eval_auto_with_gui.py as an example to get started. In this script, we instantiate a scenario for cooperative navigation (defined in src/MultiVehicleEnv/scenarios/multi_reach.py) and obtain the simulation environment to train and evaluate RL strategy.