The solution of the problem of bluff detection in the game «I-doubt-it» based on reinforcement learning

In this paper we consider the construction of an algorithm based on reinforcement learning for the problem of recognizing and using a bluff on the example of a card game «I-doubt-it». The constructed algorithm has the 'intellectual ability' to restructure its behavior strategy and to evaluate possible moves based on previous experience.This class of algorithms used to make decisions in rapidly changing environments. The method and results of comparing algorithms among themselves, the results of games of the best algorithms with a real opponent are obtained. The effect of 'overfitting' is detected, increasing the number of training batches, in some cases, does not improve, but worsens the quality of the algorithm.