Explainers
No explainer here yet. This is the kind of cell that gets one as soon as something in the queue earns it.
Unread — 6
Reward Maximisation through Discrete Active Inference
Shows active inference is Bellman-optimal only at one-step horizons, and that sophisticated inference recovers optimality over finite ones.
arXiv 2009.08111 paper · Apr 2020Whence the Expected Free Energy?
Argues expected free energy is not simply free energy in the future, and derives the FEEF alternative on firmer footing.
arXiv 2004.08128 paper · Jul 2026Active Inference as a Convex Markov Decision Process
Recasts expected free energy minimisation as a convex MDP, giving a policy-dependent reward that actor-critic can actually use.
arXiv 2607.20152 paper · Mar 2022Active Inference: The Free Energy Principle in Mind, Brain, and Behavior
The standard textbook, open access: free energy principle through to discrete and continuous-time active inference, with worked models.
MIT Press code · v1.0.3, Jun 2026pymdp — active inference for Markov Decision Processes
The reference Python library for discrete active inference: POMDP generative models, expected free energy planning, and learning.
GitHub — infer-actively/pymdp post · Oct 2020Spinning Up in Active Inference and the Free Energy Principle
A staged syllabus across the five prerequisite fields — the on-ramp your free-energy explainer assumes but does not provide.
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