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Found 8 papers, 2 papers with code
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.
The relationship between dynamic programming and active inference: the discrete, finite-horizon case
In this paper, we consider the relation between active inference and dynamic programming under the Bellman equation, which underlies many approaches to 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.
