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Related Concept Videos

Observational Learning01:12

Observational Learning

325
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
359
Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
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Purposive Learning01:22

Purposive Learning

212
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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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Domain Knowledge-Based Evolutionary Reinforcement Learning for Sensor Placement.

Mingxuan Song1, Chengyu Hu1, Wenyin Gong1

  • 1School of Computer Science, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing sensor placement in water supply networks (WSN) is crucial for rapid pollutant detection. An evolutionary reinforcement learning (ERL) algorithm effectively solves the sensor placement problem (SPP), outperforming other methods for enhanced water safety.

Keywords:
combinatorial optimizationdomain knowledgeevolutionary reinforcement learningsensor placement

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Computer Science

Background:

  • Water supply networks (WSN) are vulnerable to contamination, posing significant public health risks.
  • Real-time water quality monitoring via sensors is vital for mitigating contamination consequences.
  • Optimal sensor placement in large-scale WSN is a complex challenge due to limited resources.

Purpose of the Study:

  • To address the sensor placement problem (SPP) in water supply networks.
  • To develop an efficient algorithm for optimizing sensor deployment to reduce pollutant detection time.

Main Methods:

  • Modeled the sensor placement problem (SPP) as a sequential decision optimization task.
  • Proposed an evolutionary reinforcement learning (ERL) algorithm incorporating domain knowledge.
  • Conducted extensive experiments to evaluate algorithm performance.

Main Results:

  • The proposed ERL algorithm demonstrated superior performance in solving the SPP.
  • ERL significantly outperformed traditional meta-heuristic algorithms.
  • ERL also showed better results compared to deep reinforcement learning (DRL) approaches.

Conclusions:

  • The developed ERL algorithm offers an effective solution for optimizing sensor placement in WSN.
  • This approach can lead to reduced pollutant detection times and improved public health protection.
  • ERL provides a promising strategy for real-time water quality monitoring in critical infrastructure.