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Self-taught Neural Agents in Clever Game Playing

Abstract:

In the Tic-Tac-Toe game community, brilliant children and some adults discover, through playing experience, exceptional game situations where the current player declares him/herself a winner, no matter what the opponent does in the next moves. This paper proposes a Tic-Tac-Toe learning environment based on a self-motivated neural agent that learns by itself these exceptional game situations and then use this knowledge in real world tournaments, where it mimics a Markov model. The used reinforcement learning method involves a reward gaining strategy where indexed sub networks are noise balanced trained, as to clearly point toward found rewards, thus endorsing a successful future search for maximal recompenses. During training the agent explores far ahead movements based on the Bellman equation and memorizes game patterns that assure winning-ahead situations. During the operating phase the neural agent receives advising from the trained networks and realizes aperture moves that mimic the abilities of clever human players.