pymdp: A Python library for active inference in discrete state spaces
Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Centre for the Advanced Study of Collective Behaviour（集体行为高级研究中心）；University of Konstanz（康斯坦茨大学）；VERSES Research Lab（VERSES研究实验室）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；Northeastern University（东北大学）；University College London（伦敦大学学院）；University of Sussex（萨塞克斯大学）
Conor Heins, Beren Millidge, Daphne Demekas, Brennan Klein, Karl Friston, Iain Couzin, Alexander Tschantz
发表信息：
Journal of Open Source Software, 7(73), 4098 (2022)
Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference. Active inference has seen growing applications in academic research, especially in fields that seek to model human or animal behavior. While in recent years, some of the code arising from the active inference literature has been written in open source languages like Python and Julia, to-date, the most popular software for simulating active inference agents is the DEM toolbox of SPM, a MATLAB library originally developed for the statistical analysis and modelling of neuroimaging data. Increasing interest in active inference, manifested both in terms of sheer number as well as diversifying applications across scientific disciplines, has thus created a need for generic, widely-available, and user-friendly code for simulating active inference in open-source scientific computing languages like Python. The Python package we present here, pymdp (see https://github.com/infer-actively/pymdp), represents a significant step in this direction: namely, we provide the first open-source package for simulating active inference with partially-observable Markov Decision Processes or POMDPs. We review the package's structure and explain its advantages like modular design and customizability, while providing in-text code blocks along the way to demonstrate how it can be used to build and run active inference processes with ease. We developed pymdp to increase the accessibility and exposure of the active inference framework to researchers, engineers, and developers with diverse disciplinary backgrounds. In the spirit of open-source software, we also hope that it spurs new innovation, development, and collaboration in the growing active inference community.
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)
Andalusian Institute of Data Science and Computational Intelligence（安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）；Repsol Technology Lab（雷普索尔技术实验室）
seqme: a Python library for evaluating biological sequence design
Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所）；Technical University of Munich（慕尼黑工业大学）；University of Warsaw（华沙大学）；Warsaw University of Technology（华沙理工大学）
Paraconsistent-Lib: an intuitive PAL2v algorithm Python Library
Embedded Artificial Intelligence Laboratory (EAILab)（嵌入式人工智能实验室）；Federal Institute of Sao Paulo (IFSP)（圣保罗联邦学院）；Applied Paraconsistent Logic Group（应用次协调逻辑研究组）；Santa Cecilia University (UNISANTA)（圣塞西莉亚大学）
