fmpr / CAREL
CAREL is open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment.
☆8Updated 5 years ago
Alternatives and similar repositories for CAREL:
Users that are interested in CAREL are comparing it to the libraries listed below
- MetaLight: a value-based meta-reinforcement learning framework for traffic signal control☆39Updated 5 years ago
- ☆10Updated 2 years ago
- IPDALight for traffic signal control☆18Updated last year
- Computational framework for reinforcement learning in traffic control☆44Updated 5 years ago
- Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control☆62Updated 2 years ago
- Adds CityFlow to Gym☆27Updated 3 years ago
- ☆14Updated 3 years ago
- Traffic Signal Control Competition☆37Updated 6 years ago
- ☆39Updated 11 months ago
- ☆12Updated 4 years ago
- Python3 library able to connect the RLLIB framework with the SUMO simulator.☆26Updated 3 years ago
- ☆39Updated 5 years ago
- A Deep Reinforcement Learning Network for Traffic Light Cycle Control☆52Updated 4 years ago
- deep reinforcement learing SUMO☆44Updated 6 years ago
- This repository contains the code for the paper "UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control".☆33Updated 5 months ago
- The source code of paper "Decentralized Neighbouring Information Fusion for Traffic Network Signal Control" and related baselines.☆20Updated last year
- Fleet Management Simulation Framework☆33Updated 6 years ago
- ☆58Updated 5 years ago
- ☆64Updated 2 years ago
- codes for paper 《Neighborhood Cooperative Multiagent Reinforcement Learning for Adaptive Traffic Signal Control in Epidemic Regions》☆12Updated 3 years ago
- OpenAI gym wrapper for SUMO☆16Updated 5 years ago
- Modelling bus-on-demand using SUMO and TraCI.☆19Updated 11 years ago
- Cooperative Control of Traffic Signals and Connected Vehicles: A Multi-agent Deep Reinforcement Learning Approach☆21Updated 3 years ago
- TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed…☆92Updated 2 years ago
- Python package to process NGSIM data and traffic sensing with autonomous vehicles☆54Updated 4 years ago
- ☆26Updated 5 years ago
- ☆16Updated last year
- Transit control with RL☆9Updated last year
- This is the official implementation of "Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Le…☆33Updated 3 years ago
- Effcient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning☆49Updated 4 years ago