Rllib

RLlib is a scalable, open-source reinforcement learning library built on top of Ray. It is designed for training and deploying reinforcement learning models efficiently across single-machine and distributed environments, making it a strong choice for research and production use cases.

Features of RLlib:

• Supports popular reinforcement learning algorithms such as PPO, DQN, A3C, and SAC.
• Built on Ray for scalable distributed training and execution.
• Works with both TensorFlow and PyTorch.
• Provides a high-level API for easy model training and evaluation.
• Suitable for game AI, robotics, recommendation systems, and decision-making applications.
• Efficient resource utilization for large-scale machine learning workloads.
• Active open-source development with regular updates and community support.

To start RLlib, run:
$ python3
>>> import ray
>>> import ray.rllib
>>> print("RLlib is working")

To check the installed version of RLlib, run:
$ python3 -c "import ray; print(ray.__version__)"

Disclaimer: RLlib is an open-source library distributed as part of the Ray framework. It is provided "as is" without any warranty, express or implied. Users are responsible for proper setup, usage, and compliance with applicable open-source licensing terms.