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Found 12 papers, 2 papers with code
Active Inference and Intentional Behaviour
First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
A variational synthesis of evolutionary and developmental dynamics
This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other.
Bistable perception, precision and neuromodulation
Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations.
Active inference, Bayesian optimal design, and expected utility
Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i. e., expected utility maximization.
Bayesian brains and the Rényi divergence
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
Reward Maximisation through Discrete Active Inference
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.
Action and Perception as Divergence Minimization
While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.
Sophisticated Inference
In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.
Dynamic causal modelling of COVID-19
This technical report describes a dynamic causal model of the spread of coronavirus through a population.
Neural dynamics under active inference: plausibility and efficiency of information processing
We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space.
Active inference: demystified and compared
In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.
