Search Results for author: Linus Gisslén

Found 7 papers, 0 papers with code

Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning

no code implementations25 Aug 2022 Matilda Tamm, Olivia Shamon, Hector Anadon Leon, Konrad Tollmar, Linus Gisslén

We report promising results and envision that this method can be used to automate the process of LOD reduction testing and validation.

Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning

no code implementations15 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.

Imitation Learning

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

no code implementations21 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.

Imitation Learning reinforcement-learning

Augmenting Automated Game Testing with Deep Reinforcement Learning

no code implementations29 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.

FPS Games reinforcement-learning +1

Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents

no code implementations25 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.

reinforcement-learning

Adversarial Reinforcement Learning for Procedural Content Generation

no code implementations8 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.

reinforcement-learning

Imitation Learning with Concurrent Actions in 3D Games

no code implementations14 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.

Imitation Learning reinforcement-learning

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