Mesa is an agent-based modeling framework in Python
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Updated
Mar 9, 2023 - Python
Mesa is an agent-based modeling framework in Python
Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
Reinforcement learning environments with musculoskeletal models
Pedestrian simulator powered by the social force model
Core plug-in projects of the GAMA platform
We have used Deep Reinforcement Learning and Advanced Computer Vision techniques to for the creation of Smart Traffic Signals for Indian Roads. We have created the scripts for using SUMO as our environment for deploying all our RL models.
Cooperative Driving Dataset: a dataset for multi-agent driving scenarios
Rules-based engine for life sims, with time travel
OpenMAS is an open source multi-agent simulator based in Matlab for the simulation of decentralized intelligent systems defined by arbitrary behaviours and dynamics.
LiveTraffic is an X-Plane multiplayer plugin, which fills your sky with live air traffic based on public flight tracking data.
Whitefield provides a simulation environment for wireless sensor networks by combining RF simulation provided by NS3 and network stack provided by popular IoT OSes such as Contiki/RIOT/OpenThread.
This repository contains a c++ implementation of a control engine for LED Matrix tables. It also contains a simulation environment for desktop computers..
A Cloud/Web-Based Simulation Environment
SimViz contains tools and resources for authoring and executing autonomous vehicle simulations on roadways and city scapes by using map import, scene creation, formatting of ground truth data, and creating spline based roads.
A Framework for Generating and Executing Digital Twins
A multi-body simulation software
An agent-based modeling framework for Python with a shallow learning curve and powerful visualization capabilities.
Public releases of the FLEE agent-based modelling code.
Hakoniwa: a virtual simulation environment in the age of IoT and cloud robotics
Saving incoming camera sensor images data as Numpy arrays to generate ground truth data for semantic segmentation
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