  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.
