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Neural networks made easy (Part 34): Fully Parameterized Quantile Function

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by , 04-08-2023 at 02:37 AM (453 Views)
      
   
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We continue studying distributed Q-learning algorithms. Earlier we have already considered two algorithms. In the first one [4], our model learned the probabilities of receiving a reward in a given range of values. In the second algorithm [5], we used a different approach to solving the problem. We trained the model to predict the reward level with a given probability.
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