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Modeling cognitive behavior in AI games has got much attention in recent years. One popular and most commonly used cognitive learning technique is Reinforcement Learning (RL). It is a self-learning ability that empowers game agents to flatter as an autodidact. RL has also got much attention in learning board games. Ludo is one of the popular board games of the Asian subcontinent. Researchers primarily focused on the reward feature of RL. This varies between 0 and 1 (i.e. No reward to maximum reward). However, there is no direct concept of penalty in RL. Focus on penalties leads to efficient learning. Such that, considering the penalty before action selection may decrease the worth of actions leading to the penalty. Keeping this in view, we have proposed a Q-learning-based Intelligent Ludo Agent (ILA) that incorporates both rewards and penalties. Q-Learning is an off-policy RL technique used in Machine …