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Having multiple selves helps learning agents explore and adapt in complex changing worlds.

Zack Dulberg1, Rachit Dubey2, Isabel M Berwian1

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544.

Proceedings of the National Academy of Sciences of the United States of America
|July 3, 2023
PubMed
Summary
This summary is machine-generated.

Modular agents, designed with specialized subagents, excel at balancing multiple conflicting needs in dynamic environments. This architecture promotes intrinsic exploration and robust adaptation, outperforming traditional integrated approaches.

Keywords:
conflictexplorationmodularitymultiobjective decision-makingreinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Systems Biology

Background:

  • Adaptive agents face challenges in satisfying diverse, conflicting needs within changing environments.
  • Traditional monolithic agents integrate multiple objectives into a single measure of success.
  • Understanding agent adaptation principles can shed light on biological systems, including human cognition.

Purpose of the Study:

  • To investigate the efficacy of modular agent design for satisfying multiple conflicting needs.
  • To compare the performance of modular agents against monolithic agents in a multiobjective task.
  • To explore the underlying mechanisms contributing to modular agent robustness and scalability.

Main Methods:

  • Utilized deep reinforcement learning formalism for agent design and simulation.
  • Developed a biologically relevant multiobjective task focused on maintaining physiological homeostasis.
  • Simulated modular agents and monolithic agents across various nonstationary environments.

Main Results:

  • Modular agents demonstrated emergent, intrinsic exploration, unlike externally imposed methods.
  • Modular agents exhibited superior robustness in adapting to changing, nonstationary environments.
  • Agent performance scaled effectively with an increasing number of conflicting objectives.

Conclusions:

  • Modular agent architectures enhance the capacity to satisfy multiple, conflicting needs.
  • Intrinsic exploration and efficient representation in modular agents contribute to robustness and scalability.
  • The findings suggest modularity as a potential normative principle for adaptation in complex systems, possibly explaining human 'multiple selves'.