ArticleNeural computation2023
Reward Maximization Through Discrete Active Inference.
Article in Neural computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Conative inference as a mechanism of allostatic resilience: wanting to be and being to want.Cognitive, affective & behavioral neuroscience · 2026Review
- Agentic Finance: An Adaptive Inference Framework for Bounded-Rational Investing Agents.Entropy (Basel, Switzerland) · 2026Article
- Decision, Inference, and Information: Formal Equivalences Under Active Inference.Entropy (Basel, Switzerland) · 2025Review
- Gradient-Free De Novo Learning.Entropy (Basel, Switzerland) · 2025Article
- Sophisticated Learning: A novel algorithm for active learning during model-based planning.ArXiv · 2025Article
- Transforming Perceptions: Exploring the Multifaceted Potential of Generative AI for People With Cognitive Disabilities.JMIR neurotechnology · 2025Article
- Resilience phenotypes derived from an active inference account of allostasis.Frontiers in behavioral neuroscience · 2025Article
- When the interoceptive and conceptual clash: The case of oppositional phenomenal self-modelling in Tourette syndrome.Cognitive, affective & behavioral neuroscience · 2024Review
- Complex behavior from intrinsic motivation to occupy future action-state path space.Nature communications · 2024Article
- Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them.Frontiers in psychology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Active inference is a probabilistic framework for modeling the behavior of biological and artificial agents, which derives from the principle of minimizing free energy. In recent years, this framework has been applied successfully to a variety of situations where the goal was to maximize reward, often offering comparable and sometimes superior performance to alternative approaches. In this article, we clarify the connection between reward maximization and active inference by demonstrating how and when active inference agents execute actions that are optimal for maximizing reward. Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation, a formulation that underlies several approaches to model-based reinforcement learning and control. On partially observed Markov decision processes, the standard active inference scheme can produce Bellman optimal actions for planning horizons of 1 but not beyond. In contrast, a recently developed recursive active inference scheme (sophisticated inference) can produce Bellman optimal actions on any finite temporal horizon. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.