A short presentation on active inference
This document presents a concise mathematical formulation of Active inference. Active inference is an algorithm that aims to model adaptive behaviors and has a wide range of applications, in particular when the number of parameters used to describe a phenomenon is of reasonable size. In the simplest setting, an agent has a generative model of the time evolution of its environment and of the consequences of its actions on its environment. The agent infers beliefs about its environment through noisy observations and plans its actions in a Bayesian fashion, i.e. rewards are stochastic and best action is chosen by maximizing the likelihood of possible actions.
Recommended citation: Tsukahara, J., & Sergeant-Perthuis, G. (2024). A short presentation of Active Inference. Unpublished. https://doi.org/10.13140/RG.2.2.36031.73120. https://www.researchgate.net/publication/379840423_A_short_presentation_of_Active_Inference