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Related Concept Videos

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:
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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.
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Observational Learning01:12

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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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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.
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
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Related Experiment Video

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The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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AlphaSeq: Sequence Discovery With Deep Reinforcement Learning.

Yulin Shao, Soung Chang Liew, Taotao Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 22, 2019
    PubMed
    Summary
    This summary is machine-generated.

    AlphaSeq, a novel deep reinforcement learning (DRL) approach, discovers desired sequences by framing it as a game. This method excels at complex sequence discovery problems intractable by traditional mathematical techniques.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Sequence discovery is crucial for various applications.
    • Traditional methods struggle with complex objectives.
    • Algorithmic sequence generation is an ongoing research area.

    Purpose of the Study:

    • Introduce AlphaSeq, a new paradigm for algorithmic sequence discovery.
    • Demonstrate AlphaSeq's effectiveness in complex sequence design.
    • Apply AlphaSeq to real-world problems in communication and radar systems.

    Main Methods:

    • AlphaSeq utilizes deep reinforcement learning (DRL).
    • The problem is modeled as an episodic symbol-filling game (Markov decision process).
    • AlphaSeq adapts the AlphaGo DRL framework for sequence discovery.

    Main Results:

    • AlphaSeq successfully rediscovers ideal complementary codes for multi-carrier CDMA systems.
    • New sequences discovered by AlphaSeq triple the signal-to-interference ratio for MMF estimators in radar.
    • The algorithm learns progressively, improving sequence quality over time.

    Conclusions:

    • AlphaSeq offers a powerful algorithmic approach for discovering sequences with desired properties.
    • This DRL-based method is particularly advantageous for problems with complex, mathematically intractable objectives.
    • AlphaSeq demonstrates significant potential in optimizing signal processing applications.