2021/10/15 by Ludovic Denoyer, Denoyer, Ludovic, Alfredo de la Fuente +10 · 1 voice
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.07910
openalex publication_date 2021/10/15 · arxiv published 2021/10/15 · arxiv updated 2021/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.