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Found 7 papers, 0 papers with code
Reframing the Expected Free Energy: Four Formulations and a Unification
In the second setting, a justification of the root expected free energy definition is known, but this setting only accounts for two formulations, i. e., the risk over states plus ambiguity and entropy plus expected energy formulations.
Deconstructing deep active inference
The goal of this activity is to solve more complicated tasks using deep active inference.
Multi-Modal and Multi-Factor Branching Time Active Inference
Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity.
Branching Time Active Inference with Bayesian Filtering
Its root can be found in Active Inference (Friston et al., 2016; Da Costa et al., 2020; Champion et al., 2021c), a neuroscientific framework widely used for brain modelling, as well as in Monte Carlo Tree Search (Browne et al., 2012), a method broadly applied in the Reinforcement Learning literature.
Branching Time Active Inference: empirical study and complexity class analysis
This was enabled by the development of a variational message passing approach to active inference, which enables compositional construction of Bayesian networks for active inference.
Branching Time Active Inference: the theory and its generality
In this paper, we present an alternative framework that aims to unify tree search and active inference by casting planning as a structure learning problem.
Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
Since, variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this paper opens the door to advanced generative models for active inference.
