Search Results for author: Thomas Parr

Found 8 papers, 2 papers with code

Active inference, Bayesian optimal design, and expected utility

no code implementations21 Sep 2021 Noor Sajid, Lancelot Da Costa, Thomas Parr, Karl Friston

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

no code implementations12 Jul 2021 Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston

Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.

Bayesian Inference Variational Inference

The relationship between dynamic programming and active inference: the discrete, finite-horizon case

no code implementations17 Sep 2020 Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith

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.

Decision Making reinforcement-learning

Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

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.

Decision Making Representation Learning

Sophisticated Inference

no code implementations7 Jun 2020 Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas Parr

In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.

Active Learning

Neural dynamics under active inference: plausibility and efficiency of information processing

no code implementations22 Jan 2020 Lancelot Da Costa, Thomas Parr, Biswa Sengupta, Karl Friston

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

1 code implementation24 Sep 2019 Noor Sajid, Philip J. Ball, Thomas Parr, Karl J. Friston

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.

Atari Games OpenAI Gym +1

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