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Related Concept Videos

Observational Learning01:12

Observational Learning

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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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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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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.
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Purposive Learning01:22

Purposive Learning

214
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.
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Updated: Oct 1, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Curriculum-Based Deep Reinforcement Learning for Quantum Control.

Hailan Ma, Daoyi Dong, Steven X Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |March 9, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces curriculum-based deep reinforcement learning (CDRL) for efficient quantum system control. CDRL uses a curriculum of tasks to improve strategy optimization, outperforming traditional methods.

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    Area of Science:

    • Quantum Control
    • Machine Learning

    Background:

    • Deep reinforcement learning (DRL) offers efficient strategy design for complex systems.
    • Precise control of quantum systems remains a challenge.

    Purpose of the Study:

    • To develop a novel DRL approach for fast and precise quantum system control.
    • To introduce a curriculum-based DRL (CDRL) method for enhanced strategy optimization.

    Main Methods:

    • Constructing a curriculum of intermediate tasks defined by fidelity thresholds.
    • Utilizing knowledge transfer between successive tasks and sequencing tasks by difficulty.
    • Comparing CDRL with gradient method (GD), genetic algorithm (GA), and other DRL methods.

    Main Results:

    • CDRL demonstrates improved control performance for quantum systems.
    • The method efficiently identifies optimal strategies using fewer control pulses.
    • Outperforms traditional methods in numerical comparisons.

    Conclusions:

    • CDRL provides an effective framework for optimizing quantum system control.
    • The curriculum-based approach enhances DRL efficiency and performance in quantum applications.