Training AI agents to perform complex tasks in simulated worlds requires millions to billions
of steps of experience. To achieve high performance, today's fastest simulators for training AI …

We aim to understand how people assess human likeness in navigation produced by
people and artificially intelligent (AI) agents in a video game. To this end, we propose a …

In games, as in many other domains, design validation and testing is a significant challenge
as systems are growing in size and manual testing is becoming infeasible. In this position …

Going from research to production, especially for large and complex software systems, is
fundamentally a hard problem. In large-scale game production, one of the main reasons is …

Modern AAA video games feature huge game levels and maps, which are increasingly hard
for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs …

The game industry is challenged to cope with increasing growth in demand and game
complexity while maintaining acceptable quality standards for released games. Classic …

Standard cooperative multi-agent reinforcement learning (MARL) methods aim to find the
optimal team cooperative policy to complete a task. However there may exist multiple …

Reinforcement learning (RL) is a promising strategy for the development of autonomous
agents in various control and optimization contexts, including the generation of autonomous …

A great challenge for applying multi-agent reinforcement learning (MARL) in the field of
game AI is to enable agents to learn diversified policies to handle different gamespecific …

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