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Facebook wants to make Reinforcement Learning easier
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OCT 25, 2021
Facebook AI has announced the release of ‘SaLinA,’ a lightweight library for implementing sequential decision models, including reinforcement learning (RL) algorithms.
??According to Ludovic Denoyer, a research scientist at Facebook, “SaLinA is a Pytorch modification that allows users to combine agents instead of modules, giving the computation a time dimension. Classic RL algorithms may be constructed in a few lines using this abstraction, and they are not dependent on policy designs.”
SaLinA is a lightweight library for developing sequential decision models that extends PyTorch components. It can be used for RL, as well as in supervised/unsupervised learning scenarios.
Source: SaLinA
SaLina’s goal is to make the implementation of sequential decision processes, particularly those including RL, natural and easy for practitioners who have a working knowledge of how neural networks can be implemented. SaLina aims to handle any sequential decision problem by employing simple ‘agents’ that progressively process data. The intended audience includes researchers in natural language processing or computer vision and experts in natural language processing who are looking for a more natural way to model conversations in their models, trying to make them more straightforward and easily understood than previous methods.
By integrating agents, SaLinA enables the implementation of sequential decision-making algorithms in a novel method. It is a small library, extremely flexible, and scalable. It enables the creation of new algorithms and the rapid evaluation of novel ideas without losing training or testing speed.
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