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Modeling long-term nutritional behaviors using deep homeostatic reinforcement learning.

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Homeostatic reinforcement learning (RL) enables autonomous agents to balance multiple demands, mimicking animal foraging behaviors. This study confirms that homeostatic RL agents exhibit similar long-term foraging properties as animals, controllable via internal dynamics and motivation weighting.

Keywords:
deep reinforcement learninghomeostasisnutritionnutritional geometry framework

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Area of Science:

  • Behavioral neuroscience
  • Artificial intelligence
  • Computational biology

Background:

  • Autonomous agents and animals face conflicting demands requiring continuous behavioral adaptation.
  • Homeostatic reinforcement learning (RL) is a bio-inspired framework for multiobjective control using internal information.
  • The long-term behavioral properties of homeostatic RL agents compared to animals remain unclear.

Purpose of the Study:

  • To investigate if homeostatic RL agents exhibit similar long-term foraging characteristics as animals.
  • To utilize nutritional geometry to quantitatively analyze multi-nutrient foraging strategies.
  • To establish a verification environment for experimental comparison.

Main Methods:

  • Focusing on multi-nutrient balancing in animal foraging as a natural multiobjective control scenario.
  • Employing the nutritional geometry framework to analyze long-term foraging characteristics.
  • Constructing a verification environment to experimentally test homeostatic RL agents.

Main Results:

  • Homeostatic RL agents demonstrated long-term foraging characteristics comparable to those observed in natural animal populations.
  • Numerical simulations confirmed that agent foraging behavior is controllable by adjusting multiobjective motivation weighting.
  • Behavioral emergence at the motor control level in homeostatic RL agents was observed.

Conclusions:

  • Homeostatic RL agents can replicate the long-term foraging properties seen in animals.
  • The internal dynamics of the agent and real-time motivation weighting are key predictable and designable factors for long-term behavior.
  • This research bridges artificial intelligence and behavioral biology, offering insights into autonomous systems and natural behavior.