Computational framework for reinforcement learning in traffic control
-
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.
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.
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
Containerised SUMO. Use sumo, sumo-gui and TraCI with Docker. 🐳 🚗
Reinforcement Learning-based VANET simulations
Unified All-in-one Monero, Sumokoin, Aeon and other Cryptonote miner
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).
SUMO Pytorch Deep Reinforcement Learning Traffic Signal Control
Add a description, image, and links to the sumo topic page so that developers can more easily learn about it.
To associate your repository with the sumo topic, visit your repo's landing page and select "manage topics."