Video games have served as useful benchmarks for the decision making community, but
going beyond Atari games towards training agents in modern games has been prohibitively …

Imitation learning is an effective approach for training game-playing agents and,
consequently, for efficient game production. However, generalization-the ability to perform …

Reinforcement learning has been widely successful in producing agents capable of playing
games at a human level. However, this requires complex reward engineering, and the …

In video games, the validation of design specifications throughout the development process
poses a major challenge as the project grows in complexity and scale and purely manual …

In strategy games, one of the most important aspects of game design is maintaining a sense
of challenge for players. Many mobile titles feature quick gameplay loops that allow players …

Offline Reinforcement Learning (ORL) is a promising approach to reduce the high sample
complexity of traditional Reinforcement Learning (RL) by eliminating the need for continuous …

Recent advances in Competitive Self-Play (CSP) have achieved, or even surpassed, human
level performance in complex game environments such as Dota 2 and StarCraft II using …

This work introduces an automated testing approach that employs agents controlling game
characters to detect potential bugs within a game level. Harnessing the power of Bayesian …

Human-like agents have the potential to drastically improve multiplayer, first-person shooter
(FPS) games. They can serve as engaging teammates, useful practice partners, and anti …

We introduce a new reward function direction for intrinsically motivated reinforcement
learning to mimic human behavior in the context of computer games. Similar to previous …