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Related Experiment Video

Updated: Mar 14, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
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Published on: February 15, 2015

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Optimal Behavior is Easier to Learn than the Truth.

Ronald Ortner1

  • 1Department Mathematik und Informationstechnologie, Montanuniversität Leoben, Franz-Josef-Straße 18, 8700 Leoben, Austria.

Minds and Machines
|September 30, 2016
PubMed
Summary
This summary is machine-generated.

This study shows that while reinforcement learning can find optimal behavior, trying to identify the true model can fail. Some models are too complex for accurate identification, even with the correct one present.

Keywords:
Markov decision processesRegretReinforcement learningTruth

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Theory

Background:

  • Reinforcement learning (RL) algorithms aim to optimize decision-making through trial and error.
  • Existing RL methods can achieve optimal behavior even when the environment's true model is unknown.
  • The challenge lies in determining if model identification is necessary or even feasible.

Purpose of the Study:

  • To analyze the necessity and feasibility of identifying the true model in reinforcement learning.
  • To investigate scenarios where model identification is inherently difficult or impossible.
  • To understand the limitations of model-based reinforcement learning.

Main Methods:

  • Theoretical analysis of reinforcement learning algorithms.
  • Exploration of model selection and identification within a known set of candidate models.
  • Development of counterexamples demonstrating the failure of model identification.

Main Results:

  • Demonstrated that optimal behavior can be learned without identifying the true model.
  • Identified specific conditions where attempting to identify the true model leads to failure.
  • Showcased that complex or ambiguous models can prevent accurate model selection.

Conclusions:

  • Model identification is not always required for successful reinforcement learning.
  • Pursuing model identification can be counterproductive in certain reinforcement learning settings.
  • Focusing solely on optimal behavior, rather than model accuracy, is crucial for some RL problems.