rl is an open-source PyTorch library for Reinforcement Learning. It offers a modular, primitive-first, and Python-first approach to building and experimenting with RL algorithms.
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rl is an open-source PyTorch library for Reinforcement Learning. It offers a modular, primitive-first, and Python-first approach to building and experimenting with RL algorithms.
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About
TorchRL is an open-source Reinforcement Learning (RL) library built for PyTorch, emphasizing a modular, primitive-first, and Python-first design. It provides a comprehensive framework for developing and deploying RL agents, featuring a command-line training interface for state-of-the-art agents without extensive coding. The library also includes a revamped vLLM integration for scalable LLM inference and training, offering features like AsyncVLLM service, multiple load balancing strategies, and distributed data loading. Additionally, TorchRL offers an experimental PPOTrainer for configurable PPO training solutions and a complete LLM API for fine-tuning language models, supporting RLHF, supervised fine-tuning, and tool-augmented training. Its design principles align with the PyTorch ecosystem, ensuring efficiency, extensibility, and minimal dependencies.
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