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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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The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Reinforcement Learning in Medicine.

Anders Jonsson1

  • 1Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Kidney Diseases (Basel, Switzerland)
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

Deep reinforcement learning combines deep learning with reinforcement learning, achieving success in games. This approach shows potential for future medical applications.

Keywords:
Artificial intelligenceDeep learningReinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Reinforcement learning (RL) excels in complex game environments like Atari, Go, and chess.
  • Deep neural networks (DNNs) are key function approximators driving RL's recent successes.
  • The synergy between deep learning and RL has unlocked new capabilities.

Purpose of the Study:

  • Introduce fundamental concepts of reinforcement learning.
  • Explain the integration of reinforcement learning with deep learning.
  • Explore potential applications of deep reinforcement learning in medicine.

Main Methods:

  • Review of reinforcement learning principles.
  • Explanation of deep learning architectures for RL.
  • Discussion of deep reinforcement learning frameworks.

Main Results:

  • Demonstration of RL's efficacy in strategic game playing.
  • Highlighting the role of deep neural networks in enhancing RL performance.
  • Identification of potential medical use cases for DRL.

Conclusions:

  • Deep reinforcement learning represents a significant advancement in AI.
  • The integration of deep learning enhances RL's problem-solving capacity.
  • Deep reinforcement learning holds promise for transforming medical research and practice.