Multi Agent Reinforcement Learning for ROS in 2D Simulation Environments

Related tags

Deep Learning arena-marl
Overview

IROS21 information

To test the code and reproduce the experiments, follow the installation steps in Installation.md. Afterwards, follow the steps in Evaluations.md.

To test the different Waypoint Generators, follow the steps in waypoint_eval.md

DRL agents are located in the agents folder.

Arena-MARL

A flexible, high-performance 2D simulator with configurable agents, multiple sensors, and benchmark scenarios for testing robotic navigation in multi-agent settings.

Arena-MARL uses Flatland as the core simulator and is a modular high-level library for end-to-end experiments in embodied AI -- defining embodied AI tasks (e.g. navigation, obstacle avoidance, behavior cloning), training agents (via imitation or reinforcement learning, or no learning at all using conventional approaches like DWA, TEB or MPC), and benchmarking their performance on the defined tasks using standard metrics.
Before Training After Training

What is this repository for?

Train DRL agents on ROS compatible simulations for autonomous navigation in highly dynamic environments. Flatland-DRL integration is inspired by Ronja Gueldenring's work: drl_local_planner_ros_stable_baselines. Test state of the art local and global planners in ROS environments both in simulation and on real hardware. Following features are included:

  • Setup to train a local planner with reinforcement learning approaches from stable baselines3
  • Training in simulator Flatland in train mode
  • Include realistic behavior patterns and semantic states of obstacles (speaking, running, etc.)
  • Include different obstacles classes (other robots, vehicles, types of persons, etc.)
  • Implementation of intermediate planner classes to combine local DRL planner with global map-based planning of ROS Navigation stack
  • Testing a variety of planners (learning based and model based) within specific scenarios in test mode
  • Modular structure for extension of new functionalities and approaches

Start Guide

We recommend starting with the start guide which contains all information you need to know to start off with this project including installation on Linux and Windows as well as tutorials to start with.

  • For Mac, please refer to our Docker.

1. Installation

Please refer to Installation.md for detailed explanations about the installation process.

1.1. Docker

We provide a Docker file to run our code on other operating systems. Please refer to Docker.md for more information.

2. Usage

DRL Training

Please refer to DRL-Training.md for detailed explanations about agent, policy and training setups.

Scenario Creation with the arena-scenario-gui

To create complex, collaborative scenarios for training and/or evaluation purposes, please refer to the repo arena-scenario-gui. This application provides you with an user interface to easily create complex scenarios with multiple dynamic and static obstacles by drawing and other simple UI elements like dragging and dropping. This will save you a lot of time in creating complex scenarios for you individual use cases.

Used third party repos:

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Comments
  • Add to PettingZoo third party env list

    Add to PettingZoo third party env list

    Hey, we keep a list of PettingZoo environments here https://github.com/Farama-Foundation/PettingZoo/blob/master/docs/third_party_envs.md that shows up on the documentation website here https://www.pettingzoo.ml/third_party_envs. Would you mind creating a PR to that file with an entry briefly explaining what your environment is? I'd do it myself like normal, but I don't entirely understand what your environment is.

    opened by jkterry1 0
Owner
Robotics, Autonomous Navigation and Computer Vision Research
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