TorchRL: A Reinforcement Learning Library For PyTorch
TorchRL is an open-source library for PyTorch focused on improving the experience of working with and creating reinforcement learning models.
Created on May 16|Last edited on May 17
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TorchRL, an up-and-coming reinforcement learning library for PyTorch, is in the works. Initially arriving on GitHub a little over a month ago, they are now looking to have some attention to be thrown their way with a reddit post announcing that it's been open sourced.
While this isn't a full official release, the creators want to welcome any feedback, issues, etc. that anyone can provide, as well as encouraging contribution through repo forks and other means.
What is TorchRL best used for?
TorchRL aims to be an easy to use extension of PyTorch allowing for rapid development of reinforcement learning models. It provides low and high level abstractions to support a focused approach on reinforcement learning, while aiming to align with existing PyTorch ecosystem libraries and relying on as few dependencies as possible.
As of right now, TorchRL offers a number of utilities to make reinforcement learning quicker to get into, including a generic agent class, replay buffers, tools for interacting with common environments libraries, and more.
It's still in the early stages of development, so stability and a bug-free experience isn't guaranteed, but you can look forward to more tools, utilities, and improvements being added in the future.
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Tags: ML News
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