A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

Overview

Codacy Badge

AMAZ3DSim

AMAZ3DSim is a lightweight python-based 3D network multi-agent simulator. It uses a cell-based congestion model. It calculates risk, battery capacities, travel time and travelled distance of the agents and the loudness the network links experience. AMAZ3DSim is suitable for 3D network optimization tasks. AMAZ3DSim simulation of OSM Darmstadt scenario

Installation

Use the package manager pip to install foobar.

sudo apt install python3.8

pip install click
pip install tkinter

Usage

To start AMAZ3DSim with default settings

python3.8 CommandLineInterface.py

To open a small help doc listing the parameters of the CommandLineInterface.py

python3.8 CommandLineInterface.py --help

A full command specified all of the following paramters

python3.8 CommandLineInterface.py --in-file /path/to/scenario.xml --out-file /path/to/output.xml --configfile /path/to/config.xml --random False

If an argument is left out, a standard value is used.

Configuration

A fully commented example configuration is available under

config/config.xml

which is also the standard configuration.

Input interface of the simulator

To simulate your own network, create your own scenario.xml file. A scenario.xml contains the network, the agents and the delivery orders to be fulfilled.

Example scenario.xml files are available in the input folder.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

You might also like...
The repository forked from NVlabs uses our data. (Differentiable rasterization applied to 3D model simplification tasks)
The repository forked from NVlabs uses our data. (Differentiable rasterization applied to 3D model simplification tasks)

nvdiffmodeling [origin_code] Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Autom

An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects different compression algorithms have.
An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects different compression algorithms have.

ImageCompressionSimulation An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects o

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Matlab Python Heuristic Battery Opt - SMOP conversion and manual conversion

SMOP is Small Matlab and Octave to Python compiler. SMOP translates matlab to py

Lux AI environment interface for RLlib multi-agents
Lux AI environment interface for RLlib multi-agents

Lux AI interface to RLlib MultiAgentsEnv For Lux AI Season 1 Kaggle competition. LuxAI repo RLlib-multiagents docs Kaggle environments repo Please let

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Lightweight (Bayesian) Media Mix Model This is not an official Google product. L

A multi-entity Transformer for multi-agent spatiotemporal modeling.
A multi-entity Transformer for multi-agent spatiotemporal modeling.

baller2vec This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotempor

Comments
  • Error in scenarioParser

    Error in scenarioParser

    Starting the CommandLineInterface on default settings causes an error (see screenshot). The problem stays also with the simple scenario, but without lines 157-162 in ScenarioParser, it works.

    issue
    opened by JuliaBlome 3
  • Overloaded link lets agents freely travel despite capacity

    Overloaded link lets agents freely travel despite capacity

    An input scenario that places too many agents on a link should be handled realistically by the simulator.

    e.g. 50 agents can be placed on a capacity 1 link, but then only 1 agent should be able to move

    As of now, all agents move freely on the first link they are generated on.

    opened by danieldeer 1
Releases(v1.0.0)
Owner
Daniel Hirsch
Software Engineer @ Telespazio 🛠 M.Sc. Electrical Engineering and Information Technology (TU Darmstadt) 📀
Daniel Hirsch
《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]

Unsupervised 3D Human Pose Representation [Paper] The implementation of our paper Unsupervised 3D Human Pose Representation with Viewpoint and Pose Di

42 Nov 24, 2022
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

Res2Net The official pytorch implemention of the paper "Res2Net: A New Multi-scale Backbone Architecture" Our paper is accepted by IEEE Transactions o

Res2Net Applications 928 Dec 29, 2022
3D ResNets for Action Recognition (CVPR 2018)

3D ResNets for Action Recognition Update (2020/4/13) We published a paper on arXiv. Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, and Yutaka Satoh,

Kensho Hara 3.5k Jan 06, 2023
Instance-based label smoothing for improving deep neural networks generalization and calibration

Instance-based Label Smoothing for Neural Networks Pytorch Implementation of the algorithm. This repository includes a new proposed method for instanc

Mohamed Maher 1 Aug 13, 2022
PyTorch code for the "Deep Neural Networks with Box Convolutions" paper

Box Convolution Layer for ConvNets Single-box-conv network (from `examples/mnist.py`) learns patterns on MNIST What This Is This is a PyTorch implemen

Egor Burkov 515 Dec 18, 2022
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointCNN: Convolution On X-Transformed Points Created by Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Introduction PointCNN

Yangyan Li 1.3k Dec 21, 2022
This project provides the proof of the uniqueness of the equilibrium and the global asymptotic stability.

Delayed-cellular-neural-network This project provides the proof of the uniqueness of the equilibrium and the global asymptotic stability. There is als

4 Apr 28, 2022
Privacy-Preserving Portrait Matting [ACM MM-21]

Privacy-Preserving Portrait Matting [ACM MM-21] This is the official repository of the paper Privacy-Preserving Portrait Matting. Jizhizi Li∗, Sihan M

Jizhizi_Li 212 Dec 27, 2022
GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data This is the official PyTorch implementation of the SeCo paper: @articl

ElementAI 101 Dec 12, 2022
Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet)

Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet) Our paper: https://arxiv.org/abs/2111.13324 We will release the complet

15 Oct 17, 2022
Code and models for "Rethinking Deep Image Prior for Denoising" (ICCV 2021)

DIP-denosing This is a code repo for Rethinking Deep Image Prior for Denoising (ICCV 2021). Addressing the relationship between Deep image prior and e

Computer Vision Lab. @ GIST 36 Dec 29, 2022
This repository lets you interact with Lean through a REPL.

lean-gym This repository lets you interact with Lean through a REPL. See Formal Mathematics Statement Curriculum Learning for a presentation of lean-g

OpenAI 87 Dec 28, 2022
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning"

PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Berivan Isik 8 Dec 08, 2022
CRF-RNN for Semantic Image Segmentation - PyTorch version

This repository contains the official PyTorch implementation of the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015

Sadeep Jayasumana 170 Dec 13, 2022
This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

CLGo This is an official repository of CLGo: Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints An earlier

刘芮金 32 Dec 20, 2022
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

17 Jun 10, 2022
(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

xxxnell 656 Dec 30, 2022
This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Robust Counterfactual Explanations This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations". I

Marco 5 Dec 20, 2022
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022