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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.

De pago · 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

De pago · API

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