Automated Play-Testing Through RL Based Human-Like Play-Styles Generation

TL;DR

This paper introduces CARMI, a reinforcement learning agent that emulates human-like play-styles in video games using minimal data, enabling automated testing and balancing during game development.

Contribution

The paper presents CARMI, a novel RL-based agent that models human play-styles with limited data and generalizes to unseen levels, improving automated game testing.

Findings

CARMI accurately emulates human play-styles.

It requires only summary data and little human input.

It generalizes well to new levels.

Abstract

The increasing complexity of gameplay mechanisms in modern video games is leading to the emergence of a wider range of ways to play games. The variety of possible play-styles needs to be anticipated by designers, through automated tests. Reinforcement Learning is a promising answer to the need of automating video game testing. To that effect one needs to train an agent to play the game, while ensuring this agent will generate the same play-styles as the players in order to give meaningful feedback to the designers. We present CARMI: a Configurable Agent with Relative Metrics as Input. An agent able to emulate the players play-styles, even on previously unseen levels. Unlike current methods it does not rely on having full trajectories, but only summary data. Moreover it only requires little human data, thus compatible with the constraints of modern video game production. This novel agent could be used to investigate behaviors and balancing during the production of a video game with a realistic amount of training time.