Multi Agent Reinforcement Learning for ROS in 2D Simulation Environments

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:

Owner
Robotics, Autonomous Navigation and Computer Vision Research
Python scripts form performing stereo depth estimation using the high res stereo model in PyTorch .

PyTorch-High-Res-Stereo-Depth-Estimation Python scripts form performing stereo depth estimation using the high res stereo model in PyTorch. Stereo dep

Ibai Gorordo 26 Nov 24, 2022
A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network The official code of VisionLAN (ICCV2021). VisionLAN successfully a

81 Dec 12, 2022
A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

DaDa 106 Dec 29, 2022
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

DAGAN This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruct

TensorLayer Community 159 Nov 22, 2022
Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"

gtn_applications An applications library using GTN. Current examples include: Offline handwriting recognition Automatic speech recognition Installing

Facebook Research 68 Dec 29, 2022
[MICCAI'20] AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes

AlignShift NEW: Code for our new MICCAI'21 paper "Asymmetric 3D Context Fusion for Universal Lesion Detection" will also be pushed to this repository

Medical 3D Vision 42 Jan 06, 2023
A Jupyter notebook to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

A Jupyter notebook to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

Eugenio Herrera 175 Dec 29, 2022
Tensorflow python implementation of "Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos"

Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos This repository is the official tensorflow python implementation

Yasamin Jafarian 287 Jan 06, 2023
An open-source Kazakh named entity recognition dataset (KazNERD), annotation guidelines, and baseline NER models.

Kazakh Named Entity Recognition This repository contains an open-source Kazakh named entity recognition dataset (KazNERD), named entity annotation gui

ISSAI 9 Dec 23, 2022
This is the official repository of XVFI (eXtreme Video Frame Interpolation)

XVFI This is the official repository of XVFI (eXtreme Video Frame Interpolation), https://arxiv.org/abs/2103.16206 Last Update: 20210607 We provide th

Jihyong Oh 195 Dec 29, 2022
A set of tools for Namebase and HNS

HNS-TOOLS A set of tools for Namebase and HNS To install: pip install -r requirements.txt To run: py main.py My Namebase referral code: http://namebas

RunDavidMC 7 Apr 08, 2022
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement In this project, we proposed a Domain Disentanglement Faster-RCNN (DDF)

19 Nov 24, 2022
Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

TripClick Baselines with Improved Training Data Welcome 🙌 to the hub-repo of our paper: Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter 3 Nov 03, 2022
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

VinAI Research 42 Dec 05, 2022
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We h

97 Dec 01, 2022
nnFormer: Interleaved Transformer for Volumetric Segmentation

nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation ". Please

jsguo 610 Dec 28, 2022
Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation This is code for the ECCV Paper: Simultaneous Detection and Segmentation Bharath Hariharan, Pablo Arbelaez,

Bharath Hariharan 96 Jul 20, 2022
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Jina AI 2 Mar 15, 2022
A parametric soroban written with CADQuery.

A parametric soroban written in CADQuery The purpose of this project is to demonstrate how "code CAD" can be intuitive to learn. See soroban.py for a

Lee 4 Aug 13, 2022
Apache Flink

Apache Flink Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities. Learn more about Flin

The Apache Software Foundation 20.4k Dec 30, 2022