SaLinA: Sequential Learning of Agents

• URL: http://arxiv.org/abs/2110.07910v1
• Date: Fri, 15 Oct 2021 07:50:35 GMT
• Title: SaLinA: Sequential Learning of Agents
• Authors: Ludovic Denoyer, Alfredo de la Fuente, Song Duong, Jean-Baptiste Gaya, Pierre-Alexandre Kamienny, Daniel H. Thompson
• Abstract summary: SaLinA is a library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms. It is built as an extension of PyTorch: algorithms coded with SALINA can be understood in few minutes by PyTorch users and modified easily.
• Score: 13.822224899460656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms. It is built as an extension of PyTorch: algorithms coded with \SALINA{} can be understood in few minutes by PyTorch users and modified easily. Moreover, SaLinA naturally works with multiple CPUs and GPUs at train and test time, thus being a good fit for the large-scale training use cases. In comparison to existing RL libraries, SaLinA has a very low adoption cost and capture a large variety of settings (model-based RL, batch RL, hierarchical RL, multi-agent RL, etc.). But SaLinA does not only target RL practitioners, it aims at providing sequential learning capabilities to any deep learning programmer.

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