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Incentive Theory: Pull Theory of Motivation01:18

Incentive Theory: Pull Theory of Motivation

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Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Motivation is a multifaceted process that drives behavior toward fulfilling various physiological or psychological needs. This process involves initiating, guiding, and maintaining specific actions influenced by internal and external factors. For example, when someone feels hungry while watching television, hunger is a motivator, prompting the individual to get up, walk to the kitchen, and find something to eat. In this instance, hunger initiates and sustains the behavior necessary to meet the...
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Drive-Reduction Theory: Push Theory of Motivation01:27

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Clark Hull's drive-reduction theory, introduced in the 1940s and 1950s and often termed the "push theory" of motivation, provides a framework for understanding how biological and learned drives influence behavior. Hull suggested that motivation originates from the need to alleviate physiological tension caused by unmet biological necessities. The theory proposes that when a basic need, such as hunger or sleep, goes unfulfilled, it creates an internal imbalance. This imbalance, or...
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Behaviorists view personality as primarily shaped by environmental reinforcements and consequences. According to this perspective, behavior is influenced by external stimuli, and individuals adjust their actions based on rewards and punishments. Over time, learning histories — accumulated patterns of reinforcement — play a significant role in shaping personality. Behaviors that lead to positive outcomes are reinforced, while those resulting in negative outcomes are diminished.
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An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey.

Arthur Aubret1, Laetitia Matignon1, Salima Hassas1

  • 1Univ Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, 69622 Villeurbanne, France.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
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Deep reinforcement learning (DRL) faces challenges in action abstraction and exploration. Intrinsic motivation (IM) using information theory concepts like novelty and surprise can build transferable skills for robust exploration.

Keywords:
deep reinforcement learningdevelopmental learninginformation theoryintrinsic motivation

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • The field of reinforcement learning (RL), particularly deep reinforcement learning (DRL), is rapidly advancing with numerous contributions.
  • Significant challenges remain, including the ability to abstract actions and effective environment exploration in sparse-reward settings.
  • Intrinsic motivation (IM) offers a promising approach to address these exploration and abstraction difficulties.

Purpose of the Study:

  • To survey existing research works on intrinsic motivation in reinforcement learning.
  • To propose a novel taxonomy for analyzing IM methods based on information theory.
  • To computationally revisit concepts of surprise, novelty, and skill-learning within this framework.

Main Methods:

  • A systematic survey of reinforcement learning and intrinsic motivation research.
  • Development of an information-theoretic taxonomy to categorize and analyze methods.
  • Computational revisiting of surprise, novelty, and skill-learning metrics.

Main Results:

  • Identification of the advantages and disadvantages of various intrinsic motivation approaches.
  • Analysis of current research trends and future outlooks in the field.
  • Demonstration that novelty and surprise can facilitate the construction of hierarchical, transferable skills.

Conclusions:

  • Intrinsic motivation, particularly leveraging novelty and surprise, is crucial for advancing reinforcement learning.
  • These information-theoretic concepts aid in abstracting environmental dynamics, leading to more robust exploration strategies.
  • The proposed taxonomy provides a structured way to understand and develop future intrinsic motivation techniques.