Development of a Computer Player for Seejeh (A.K.A Seega, Siga, Kharbga) Board Game with Deep Reinforcement Learning

Abstract

Recent years have proven the existing room of deep reinforcement learning (DRL) applications. DRL has been utilized as an AI computer player in many board games. Seejeh is an ancient board game, where no one attempts to create an AI system that is able to learn to play it. Seejeh is a two-player, zero-sum, discrete, finite and deterministic game of perfect information. Seejeh board game is different from all other strategic board games. It has two stages; Positioning and moving. Player place two tiles at each action in stage one. A player might have a sequence of moves in the second stage unlike Othello and Go. In this work, we develop an automated player based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. This paper presents a self-play algorithm utilizing DRL and search algorithms. The system starts with a neural network that knows nothing about the game of Seejeh. It then plays games against itself, by combining this neural network with powerful search algorithms. To the best of our knowledge, we are the first who develop an agent that learns to play Seejeh game.