The Free Energy Principle for Perception and Action: A Deep Learning Perspective

Area of Science:

• Computational neuroscience
• Artificial intelligence
• Machine learning

Background:

• The free energy principle posits that biological agents minimize free energy to maintain preferred states.
• Active inference, a corollary, involves agents learning generative models and planning actions for homeostasis.
• This framework is computationally feasible due to variational inference and amortized planning.

Purpose of the Study:

• To investigate the application of deep learning for designing artificial agents based on active inference.
• To provide a deep-learning-centric overview of the free energy principle.
• To bridge theoretical active inference with practical deep learning implementations.

Main Methods:

• Surveying relevant literature in machine learning and active inference.
• Presenting a deep learning-oriented perspective on the free energy principle.
• Discussing design choices for implementing active inference agents using deep learning.

Main Results:

• Demonstrated the potential of deep learning for realizing active inference agents.
• Provided a unified view of the free energy principle for deep learning practitioners.
• Identified key design considerations for practical implementations.

Conclusions:

• Deep learning offers a powerful toolset for building artificial agents grounded in the free energy principle.
• This work serves as a practical guide for newcomers to active inference and a starting point for deep learning researchers.
• The integration of deep learning and active inference opens new avenues for creating intelligent, self-sustaining artificial systems.