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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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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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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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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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A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems.

Yu-Sin Kim1, Jeong-Min Lee2, Jong-Yeol Ryu2

  • 1Algorithm Team, Carvi, Seoul 08513, Korea.

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

This study enhances video streaming efficiency using XOR coding and a novel decentralized cache update scheme. The proposed K-actor-critic network reduces transmissions by up to 24% for improved video delivery.

Keywords:
cacheexclusive ORmultimediareinforcement learningstreaming

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Information Technology

Background:

  • Increasing demand for video streaming necessitates efficient delivery technologies.
  • Exclusive OR (XOR) coding enables simultaneous transmission of multiple video contents, enhancing efficiency.
  • Client-side caching is crucial for optimizing video streaming performance.

Purpose of the Study:

  • To improve video streaming efficiency using client cache storage with XOR coding.
  • To propose and evaluate a novel, decentralized cache update scheme based on reinforcement learning.
  • To analyze the performance of the proposed scheme in terms of transmission reduction.

Main Methods:

  • Investigated XOR coding-based video streaming with client cache utilization.
  • Developed a K-actor-critic (K-AC) network for a decentralized cache update scheme.
  • Trained the K-AC network using only locally available client information.
  • Analyzed performance by comparing the average number of transmissions against conventional schemes.

Main Results:

  • The proposed K-AC cache update scheme significantly enhances video streaming efficiency.
  • Numerical results demonstrate a reduction in transmissions by up to 24%.
  • The scheme is effective under specific conditions: 100 videos, 50 clients, and a cache size of 5.

Conclusions:

  • The decentralized K-AC cache update scheme offers a substantial improvement in video streaming efficiency.
  • This approach effectively reduces the number of transmissions required for XOR coding-based video delivery.
  • The findings highlight the potential of reinforcement learning in optimizing decentralized network systems for content delivery.