Automated Open World Gameplay Exploration with Curiosity-driven Reinforcement Learning

Computer games are one of the main use cases for AMD graphics cards. To ensure the highest performance and quality possible, AMD has to test its graphics hardware and software on dozens of game titles and hundreds of system configurations. While some game titles provide built-in automated benchmarks, the majority of gameplay testing is manual and results in significant effort expended.
Open world games, in which players have the freedom to explore a large and expansive map in a non-linear fashion, have seen a growth in prominence in the gaming industry in recent years. Maximizing exploration in these worlds is beneficial for testing due to the wide diversity of gameplay scenes and scenarios the player may encounter. Accomplishing this through manual game-testing is a difficult and time-consuming process, so an automated solution to maximize exploration is ideal.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

AMD Canada

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université de Montréal

Program: