The purpose of this study is understanding Q-learning through the theory that structures Reinforcement Learning and implementing Q-learning in a Third Person Shooter game. Markov Decision Processes are used as frameworks to solve Reinforcement Learning problems. The methods that are covered in this research are categorized into Dynamic Programming, Monte Carlo, or Temporal Difference. The three methods are used to derive the structure of Q-learning. The Third Person Shooter game is created using the Unity Game Engine. The game is modified to fit the Q-learning method and train the game enemy to play against the player. The training was performed over 6 weeks and results showed a satisfactory increase in the AI performance over the weeks. The results demonstrated that Q-learning successfully improved the enemy AI through a better approximation of the optimal policy.


Sommer, Nathan


Computer Science


Q-learning, Unity, Reinforcement Learning, Third Person Shooter

Publication Date


Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis



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