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Neural networks made easy (Part 29): Advantage Actor-Critic algorithm

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by , 12-02-2022 at 01:12 AM (293 Views)
      
   
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We continue to explore reinforcement learning methods. In previous articles we discussed methods for approximating the Q-learning Reward function and the policy gradient function learning. Each method has its own advantages and disadvantages. It would be great to use the maximum of their advantages when building and training models. When trying to find methods minimizing the shortcomings of the algorithms used, we often try to build certain conglomerates from various known algorithms and methods. In this article, we will talk about a way of combining the above two algorithms into a single model training method, which is called Advantage Actor-Critic algorithm).
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  1. Neural networks made easy
  2. Neural networks made easy (Part 2): Network training and testing
  3. Neural networks made easy (Part 3): Convolutional networks
  4. Neural networks made easy (Part 4): Recurrent networks
  5. Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
  6. Neural networks made easy (Part 6): Experimenting with the neural network learning rate
  7. Neural networks made easy (Part 7): Adaptive optimization methods
  8. Neural networks made easy (Part 8): Attention mechanisms
  9. Neural networks made easy (Part 9): Documenting the work
  10. Neural networks made easy (Part 10): Multi-Head Attention
  11. Neural networks made easy (Part 11): A take on GPT
  12. Neural networks made easy (Part 12): Dropout
  13. Neural networks made easy (Part 13): Batch Normalization
  14. Neural networks made easy (Part 14): Data clustering
  15. Neural networks made easy (Part 15): Data clustering using MQL5
  16. Neural networks made easy (Part 16): Practical use of clustering
  17. Neural networks made easy (Part 17): Dimensionality reduction
  18. Neural networks made easy (Part 18): Association rules
  19. Neural networks made easy (Part 19): Association rules using MQL5
  20. Neural networks made easy (Part 20): Autoencoders
  21. Neural networks made easy (Part 21): Variational autoencoders (VAE)
  22. Neural networks made easy (Part 22): Unsupervised learning of recurrent models .....
  23. Neural networks made easy (Part 23): Building a tool for Transfer Learning
  24. Neural networks made easy (Part 24): Improving the tool for Transfer Learning
  25. Neural networks made easy (Part 25): Practicing Transfer Learning
  26. Neural networks made easy (Part 26): Reinforcement Learning
  27. Neural networks made easy (Part 27): Deep Q-Learning (DQN)
  28. Neural networks made easy (Part 28): Policy gradient algorithm
  29. Neural networks made easy (Part 29): Advantage Actor-Critic algorithm

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