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Creation and Implementation of a Set of Game Strategies Based on Training Neural Networks with Reinforcement Learning

EasyChair Preprint 7095

6 pagesDate: November 28, 2021

Abstract

The study explores the problems of reinforcement learning and finding non-obvious play strategies using reinforcement learning. Two approaches to agent training (blind and pattern-based) are considered and implemented. The advantage of the self-learning approach with reinforcement using patterns as applied to a specific game (tic-tac-toe five in a row) is shown. Recorded and analyzed the use of unusual strategies by an agent using a pattern-based approach.

Keyphrases: Artificial Intelligence, Reinforcment learning, multi-agent interaction, neural network

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:7095,
  author    = {Dmitry Kozlov and Olga Polovikova},
  title     = {Creation and Implementation of a Set of Game Strategies Based on Training Neural Networks with Reinforcement Learning},
  howpublished = {EasyChair Preprint 7095},
  year      = {EasyChair, 2021}}
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