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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing.

Ruozhang Xi1, Yao Ni2, Wangyu Wu3

  • 1Krieger School of Arts and Sciences, Johns Hopkins University, Washington, DC 20001, USA.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an information-theoretic framework for reinforcement learning, using the Information Bottleneck principle to improve exploration in complex environments. The novel approach enhances learning efficiency and solution quality for challenging routing problems.

Keywords:
combinatorial routing problemscuriosity-driven explorationinformation bottleneckintrinsically-motivated reinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Theory

Background:

  • Intrinsic motivation is key for reinforcement learning (RL) exploration when rewards are scarce.
  • Defining novelty in high-dimensional state spaces is challenging for traditional RL methods.

Purpose of the Study:

  • To propose an information-theoretic framework for intrinsically motivated RL using the Information Bottleneck principle.
  • To develop a method for learning compact latent state representations that balance observation compression and predictive information.

Main Methods:

  • Utilized the Information Bottleneck principle to create latent state representations.
  • Defined intrinsic rewards based on mutual information within the latent space.
  • Employed neural mutual information estimators for scalable estimation in high-dimensional settings.

Main Results:

  • The proposed method demonstrated improved exploration efficiency.
  • Enhanced training stability and solution quality were observed compared to standard RL baselines.
  • Effective evaluation on combinatorial routing problems like TSP and SDVRP.

Conclusions:

  • Information bottleneck-driven intrinsic motivation offers a principled approach for RL exploration.
  • The framework effectively addresses challenges in high-dimensional and combinatorial state spaces.
  • This method advances the state-of-the-art in intrinsically motivated reinforcement learning.