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Title:
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A DEEP REINFORCEMENT LEARNING APPROACH TO THE ANCIENT INDIAN GAME - CHOWKA BHARA |
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Author(s):
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Annapurna P. Patil, Sanjay Raghavendra, Shruthi Srinarasi and Reshma Ram |
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ISBN:
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978-989-8704-41-2 |
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Editors:
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Katherine Blashki |
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Year:
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2022 |
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Edition:
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Single |
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Keywords:
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Reinforcement Learning, Chowka Bhara, Strategic Player, Q-Learning Player, Indian Board Game |
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Type:
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Short Paper |
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First Page:
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216 |
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Last Page:
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220 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Reinforcement Learning (RL) is the study of how Artificial Intelligence (AI) agents learn to make their own decisions in an environment to maximize the cumulative reward received. Although there has been notable progress in the application of RL for games, the category of ancient Indian games has remained almost untouched. Chowka Bhara is one such ancient Indian board game. This work aims at developing a Q-Learning-based RL Chowka Bhara player whose strategies and methodologies are obtained from three Strategic Players viz. Fast Player, Random Player, and Balanced Player. It is observed through the experimental results that the Q-Learning Player outperforms all three Strategic Players. |
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