no code implementations • 19 Jul 2023 • Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini, Andrew Eakins, Linus Gisslen
Going from research to production, especially for large and complex software systems, is fundamentally a hard problem.
no code implementations • 15 Aug 2022 • Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov, Linus Gisslén
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible.
no code implementations • 21 Feb 2022 • Alessandro Sestini, Linus Gisslén, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments.
no code implementations • 29 Mar 2021 • Joakim Bergdahl, Camilo Gordillo, Konrad Tollmar, Linus Gisslén
General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data.
no code implementations • 25 Mar 2021 • Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar, Linus Gisslén
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.
no code implementations • 8 Mar 2021 • Linus Gisslén, Andy Eakins, Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar
We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with an auxiliary input as a control variable.
no code implementations • 14 Mar 2018 • Jack Harmer, Linus Gisslén, Jorge del Val, Henrik Holst, Joakim Bergdahl, Tom Olsson, Kristoffer Sjöö, Magnus Nordin
This initial training technique kick-starts TD learning and the agent quickly learns to surpass the capabilities of the expert.