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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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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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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 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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Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
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Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning.

Dejan Dašić1,2,3, Nemanja Ilić1,4, Miljan Vučetić1

  • 1Artificial Intelligence Department, Vlatacom Institute, 11070 Belgrade, Serbia.

Sensors (Basel, Switzerland)
|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces a novel consensus-based algorithm for distributed spectrum sensing and channel selection in cognitive radio networks. The decentralized approach enhances collaboration and enables optimal strategy calculation, even with limited local information.

Keywords:
cognitive radio networkingconsensus algorithmdistributed Q-learningdistributed policy evaluationjoint spectrum sensing and channel selectionmulti-agent reinforcement learningoff-policy temporal difference

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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • Cognitive radio networks (CRNs) require efficient spectrum sensing and channel selection for dynamic spectrum access.
  • Decentralized and distributed algorithms are crucial for CRNs operating in complex, real-world scenarios.
  • Existing methods often rely on centralized control, which can be a bottleneck and single point of failure.

Purpose of the Study:

  • To propose a novel distributed algorithm for spectrum sensing and channel selection in CRNs.
  • To leverage a consensus strategy within a multi-agent reinforcement learning framework.
  • To enable decentralized collaboration for optimal joint spectrum sensing and channel selection.

Main Methods:

  • A consensus-based strategy implemented over a sparse, time-varying communication network.
  • Multi-agent reinforcement learning to facilitate decentralized decision-making.
  • Analysis of algorithm characteristics including denoising, coordinated actions, and convergence rates.

Main Results:

  • The proposed algorithm enables agents to calculate optimal joint spectrum sensing and channel selection strategies without individual optimality.
  • The approach is scalable and robust to node and link failures.
  • Simulations show high effectiveness, closely mimicking centralized schemes even with sparse communication.

Conclusions:

  • The consensus-based distributed algorithm offers a viable and effective solution for spectrum sensing and channel selection in CRNs.
  • Decentralized operation enhances network robustness and scalability.
  • The algorithm achieves near-optimal performance comparable to centralized systems in practical CRN environments.