RLTQC: Reinforcement Based Tabular Q-Learning for Cowry Game

The Cowry game is a board game of imperfect information that originated in India. It is a game of chance where the player's pieces move along a specified path based on the roll of special dice, known as Cowry shells. This paper proposes using the Q-learning algorithm, an off-policy reinforcement learning (RL) algorithm, for the Cowry game. We implemented a Q-learning-based Cowry player, which learns to play by engaging in a series of games against opponent players. The results show that our Q-learning-based Cowry player outperforms the opponents with a significant winning margin, assuming the opponents follow either a random strategy or a hind-most strategy.