SaLinA: Sequential Learning of Agents
This is an incremental tool that reduces adoption costs for RL practitioners and extends sequential learning capabilities to general deep learning programmers.
SaLinA is a library that simplifies the implementation of complex sequential learning models, including reinforcement learning, by extending PyTorch for ease of use and scalability across multiple CPUs and GPUs.
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.