RL4CO is a research framework for applying reinforcement learning to combinatorial optimization tasks such as the Traveling Salesman Problem, Vehicle Routing Problem, and scheduling. It provides implementations of RL algorithms, attention-based neural architectures, and benchmark environments built on PyTorch Lightning and TorchRL. The library supports benchmarking, hyperparameter management via Hydra, and standardized evaluation of learned policies.