Computational framework for reinforcement learning in traffic control
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Updated
Jul 27, 2024 - Python
Computational framework for reinforcement learning in traffic control
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.
Official github page of UCF SST CitySim Dataset
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.
A benchmark towards generalizable reinforcement learning for autonomous driving.
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
Reinforcement Learning-based VANET simulations
Reinforcement Learning + traffic microsimulation (via SUMO). Uses Ray RLLIB and forces SUMO into the OpenAI Gym Framework
applying multi-agent reinforcement learning for highway-merging autonomous vehicles
An Activity-based Multi-modal Mobility Scenario Generator for SUMO. This project is available in the Eclipse SUMO contributed tools section (https://github.com/eclipse/sumo/tree/master/tools/contributed) under the name SAGA (SUMO Activity GenerAtion).
This project simulates deployment and migration of Service Function Chains (SFC) in data-centers.
Traffic Control Test Bed
We provide an open source software package for AV based simulation and testing running a docker container
Toolbox for Map Conversion and Scenario Creation for Autonomous Vehicles.
An environment-agnostic framework for comparing intersection control algorithms
Python Parking Monitoring Library for SUMO
Berlin Sumo Traffic (BeST) Scenario
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