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Deep reinforcement learning (DRL) agents can exhibit planning behaviors without explicit models. Neuroethology tools reveal hidden structures in DRL agent learning and behavior.

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Cognitive Science

Background:

  • Standard methods for analyzing deep reinforcement learning (DRL) agent behavior are underdeveloped, especially for complex tasks.
  • Understanding agent behavior requires more than reward curve comparisons.

Purpose of the Study:

  • To apply neuroethology tools to analyze DRL agents in a complex environment.
  • To uncover structured, planning-like behaviors in DRL agents and develop a general analysis framework.

Main Methods:

  • Developed ForageWorld, a novel partially observable environment simulating real-world foraging challenges.
  • Applied joint behavioral and neural analysis inspired by neuroscience and ethology.
  • Distilled analysis tools into a general framework linking behavioral and representational features to diagnostic methods.

Main Results:

  • Model-free RNN-based DRL agents demonstrated structured, planning-like behavior through emergent dynamics.
  • Analysis revealed rich structure in DRL agent learning dynamics, previously invisible.
  • The study provides a reusable framework for analyzing a wide range of DRL agents and tasks.

Conclusions:

  • Studying DRL agents using neuroethology-inspired tools offers deeper insights into their behavior and learning.
  • Bridging AI, neuroscience, and cognitive science is crucial for understanding and aligning complex autonomous agents.
  • Emergent planning behaviors in DRL agents challenge common assumptions about the need for explicit memory or world models.