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

EasyChair Preprint 7095

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

@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}}