Date of Award
This project seeks to use reinforcement learning to develop AI agents used to controlled NPCs in video game worlds that are capable of mastering decision tasks in their video game environments. Our job will be to develop algorithms and methods that can effectively train the AI agents using Reinforcement learning, which can be used in various gaming environments and scenarios such as racing games and first-person shooters. We then market these agents to video game developers for use in their game worlds. The developer can use our agents as-is in their game without modifications or they can train them further, using our algorithms, to tune the AI agents with various behaviours and capability with minimal or no need to write the code themselves. With the use of reinforcement learning, our AI agents will learn using trial and error with rewards used to provide feedback to the AI. Over time the AI will master its environment and other AI and even possibly interaction with the human-gamer. This will produce AI controlled NPCs that behave and interact convincingly with their environments and the player, promoting player immersions while reducing developer workload.
Basilio Ferreira, Jozimar; Chibuike-Eruba, Nicholas; González Anavia, José Fernando; and Sillo, Jolomi (Oritsejolomi), "Problem Solving for Industry" (2022). ICT. 29.