ai4co/rl4co
A PyTorch library implementing reinforcement learning algorithms for solving combinatorial optimization problems.
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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.
Frequently asked
- What is ai4co/rl4co?
- A PyTorch library implementing reinforcement learning algorithms for solving combinatorial optimization problems.
- Is rl4co open source?
- Yes — ai4co/rl4co is open source, released under the MIT license.
- What language is rl4co written in?
- ai4co/rl4co is primarily written in Python.
- How popular is rl4co?
- ai4co/rl4co has 892 stars on GitHub.
- Where can I find rl4co?
- ai4co/rl4co is on GitHub at https://github.com/ai4co/rl4co.