The following thesis will present the implementation of an Artificial-Intelligence (AI) agent for a 2D fighting game that is developed using Unity, reinforcement learning (RL) is implemented in the form of Unity’s Machine Learning (ML) Agent. The thesis report first surveys AI, ML, RL algorithms and said algorithms’ application in video games, followed by the design of the game environment, state/action/reward representations, as well as the integration of the agent, training, and hyper-parameter tuning which is also discussed. The agent in question will be evaluated in the thesis to examine performance and adaptability, the RL agent learned meaningful tactics, adapted to the player’s patterns through countering the player and throughout testing provided a more engaging experience compared to the previous static AI in the prototype video game, the limitations and directions taken for the duration of the project are discussed.