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Adaptive algorithms for shaping behavior.

William L Tong1,2, Venkatesh N Murthy2,3,4, Gautam Reddy4,5,6

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|September 12, 2025
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Summary
This summary is machine-generated.

This study introduces a teacher-student framework for effective animal and AI training, balancing task difficulty to optimize learning. The adaptive shaping heuristic trains agents efficiently, even with delayed rewards.

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Area of Science:

  • Artificial Intelligence
  • Animal Behavior
  • Machine Learning

Background:

  • Behavioral shaping is a common training method for both animals and artificial agents.
  • Effective shaping strategies rely on adapting task difficulty based on learner performance.

Purpose of the Study:

  • To propose a computational framework for understanding and optimizing shaping strategies.
  • To develop an adaptive shaping heuristic for efficient behavior training.

Main Methods:

  • A teacher-student framework was developed where an autonomous agent selects tasks based on learner history.
  • Monte Carlo planning under uncertainty was used to analyze shaping algorithms.
  • An adaptive shaping heuristic was derived and tested on sequence learning and navigation tasks.

Main Results:

  • Near-optimal shaping algorithms balance reinforcement and extinction by adapting learning rates.
  • The derived heuristic demonstrated near-optimal performance on a sequence learning task.
  • The heuristic robustly trained deep reinforcement learning agents in navigation tasks with sparse, delayed rewards.

Conclusions:

  • The proposed teacher-student framework offers a general computational approach to behavior shaping.
  • Adaptive shaping is effective for training both biological and artificial agents, particularly in complex environments.
  • This work lays the foundation for more sophisticated curricula and adaptive learning systems.