Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents

by   Camilo Gordillo, et al.

As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issue is properly identified and fixed. Attempting to maximize testing coverage using only human participants, however, results in a tedious and hard to orchestrate process which normally slows down the development cycle. Complementing playtesting via autonomous agents has shown great promise accelerating and simplifying this process. This paper addresses the problem of automatically exploring and testing a given scenario using reinforcement learning agents trained to maximize game state coverage. Each of these agents is rewarded based on the novelty of its actions, thus encouraging a curious and exploratory behaviour on a complex 3D scenario where previously proposed exploration techniques perform poorly. The curious agents are able to learn the complex navigation mechanics required to reach the different areas around the map, thus providing the necessary data to identify potential issues. Moreover, the paper also explores different visualization strategies and evaluates how to make better use of the collected data to drive design decisions and to recognize possible problems and oversights.


page 1

page 2

page 3

page 5

page 6

page 7

page 8


CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories

This paper proposes a novel deep reinforcement learning algorithm to per...

Augmenting Automated Game Testing with Deep Reinforcement Learning

General game testing relies on the use of human play testers, play test ...

Rinascimento: searching the behaviour space of Splendor

The use of Artificial Intelligence (AI) for play-testing is still on the...

Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games

Going from research to production, especially for large and complex soft...

Exploration and Incentives in Reinforcement Learning

How do you incentivize self-interested agents to explore when they prefe...

Preference-conditioned Pixel-based AI Agent For Game Testing

The game industry is challenged to cope with increasing growth in demand...

Inroads into Autonomous Network Defence using Explained Reinforcement Learning

Computer network defence is a complicated task that has necessitated a h...

Please sign up or login with your details

Forgot password? Click here to reset