Active Inference, homeostatic regulation and adaptive behavioural control.

TL;DR: In this article , the authors proposed a joint information-seeking and reward maximization framework for partially observable Markov decision processes (POMDPs), where agents do not receive the true and complete state of the environment.

TL;DR: Over-flexibility in the definition of Friston blankets obscures a key distinction between observational and interventional inference, which locates the blanket in the eye of the beholder.

TL;DR: This study applies the predictive processing framework to mental health and well-being, proposing that error dynamics in affective states regulate action and predict happiness and well-being, offering a new neurocomputational framework for human flourishing.

TL;DR: In this paper , an updated hierarchical predictive coding framework for pain perception was proposed and discussed its related computational, algorithmic, and implementation issues, and active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

TL;DR: It is proposed that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them, which provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task.

TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.

TL;DR: Findings in this work indicate that dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events can be understood through quantitative theories of adaptive optimizing control.