3 citations · 7 across the 6 of their papers we have counts for
3 papers · 1 filter
Structure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning
Théophile Champion, Marek Grześ, Howard Bowman
Model-based reinforcement learning refers to a set of approaches capable of sample-efficient decision making, which create an explicit model of the environment. This model can subs…
Branching Time Active Inference with Bayesian Filtering
Théophile Champion, Marek Grześ, Howard Bowman
Branching Time Active Inference (Champion et al., 2021b,a) is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in Active Infer…
Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
Théophile Champion, Marek Grześ, Howard Bowman
Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunatel…