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.

A framework where a deep Q-Learning Reinforcement Learning agent tries to choose the correct traffic light phase at an intersection to maximize traffic efficiency.

Official github page of UCF SST CitySim Dataset

Veins - The open source vehicular network simulation framework.

Sumo is a library that prepares for fast upload for iOS. It is effective when uploading by selecting images continuously.

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 multi-stack, ETSI compliant, V2X framework for ns-3.

Luxembourg SUMO Traffic (LuST) Scenario

Monaco SUMO Traffic (MoST) Scenario

A benchmark towards generalizable reinforcement learning for autonomous driving.

Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning

Containerised SUMO. Use sumo, sumo-gui and TraCI with Docker. 🐳 🚗

Reinforcement Learning-based VANET simulations

Reinforcement Learning + traffic microsimulation (via SUMO). Uses Ray RLLIB and forces SUMO into the OpenAI Gym Framework

Unified All-in-one Monero, Sumokoin, Aeon and other Cryptonote miner

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).

Lane Changer Agent DQN

SUMO Pytorch Deep Reinforcement Learning Traffic Signal Control