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Deconstructing Deep Active Inference: A Contrarian Information Gatherer.

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Deep active inference agents were developed using deep learning. Maximizing rewards, rather than minimizing expected free energy, enabled agents to solve complex tasks by encouraging exploration.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Robotics

Background:

  • Active inference offers a unified theory for perception, learning, and decision-making.
  • Advancements in deep learning and Monte Carlo tree search aim to enhance active inference capabilities for complex tasks.

Purpose of the Study:

  • To develop and evaluate deep active inference agents for solving complex tasks.
  • To investigate the impact of different objective functions (minimizing expected free energy vs. maximizing rewards) on agent performance.
  • To analyze the role and formulation of epistemic value in deep active inference.

Main Methods:

  • Implementation of a variational autoencoder (VAE).
  • Development of deep hidden Markov models (HMMs), including a deep critical hidden Markov model (CHMM).
  • Experimentation with CHMM versions minimizing expected free energy (CHMM[EFE]) and maximizing rewards (CHMM[reward]), using various action selection strategies.

Main Results:

  • CHMM agents maximizing rewards successfully solved the dSprites environment, unlike the CHMM minimizing expected free energy.
  • The CHMM[EFE] agent converged to a single action, hindering exploration and task completion.
  • The CHMM[reward] agent demonstrated effective exploration by utilizing all actions.
  • Degenerate epistemic value formulations were observed, potentially reducing information gain.

Conclusions:

  • Maximizing rewards is crucial for enabling exploration and solving complex tasks in deep active inference.
  • The formulation of epistemic value in deep active inference requires further investigation to ensure effective information gain and avoid degenerate behavior.
  • The study highlights the limitations of minimizing expected free energy in certain exploration-dependent tasks.