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Ray RLlib

Ray RLlib is a reinforcement learning library designed for scalable, distributed execution. It supports multi-agent setups and large-scale parallel training across computing clusters. The library integrates with both TensorFlow and PyTorch, providing backend compatibility for various deep learning frameworks. RLlib offers a unified API for a range of reinforcement learning algorithms. It is part of the broader Ray ecosystem, which includes libraries for data processing, machine learning training, hyperparameter tuning, and model serving.

Payant · API

Overview

Ray RLlib is a reinforcement learning library designed for scalable, distributed execution. It supports multi-agent setups and large-scale parallel training across computing clusters. The library integrates with both TensorFlow and PyTorch, providing backend compatibility for various deep learning frameworks.

RLlib offers a unified API for a range of reinforcement learning algorithms. It is part of the broader Ray ecosystem, which includes libraries for data processing, machine learning training, hyperparameter tuning, and model serving.

Ray RLlib

Payant · API

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