RL4CO¶
An extensive Reinforcement Learning (RL) for Combinatorial Optimization (CO) benchmark. Our goal is to provide a unified framework for RL-based CO algorithms, and to facilitate reproducible research in this field, decoupling the science from the engineering.
RL4CO is built upon:
TorchRL: official PyTorch framework for RL algorithms and vectorized environments on GPUs
TensorDict: a library to easily handle heterogeneous data such as states, actions and rewards
PyTorch Lightning: a lightweight PyTorch wrapper for high-performance AI research
Hydra: a framework for elegantly configuring complex applications
Getting started:
Tutorials:
Algorithms:
Environments:
- Base Environment
- EDA Problems
- Routing Problems
- Asymmetric Traveling Salesman Problem (ATSP)
- Capacitated Vehicle Routing Problem (CVRP)
- Multiple Traveling Salesman Problem (mTSP)
- Orienteering Problem (OP)
- Pickup and Delivery Problem (PDP)
- Prize Collecting Traveling Salesman Problem (PCTSP)
- Split Delivery Vehicle Routing Problem (SDVRP)
- Stochastic Prize Collecting Traveling Salesman Problem (SPCTSP)
- Traveling Salesman Problem (TSP)
- Scheduling Problems
Models:
Additional API:
General Information:
