5 citations · 12 across the 14 of their papers we have counts for
15 papers
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jannik Hösch, Alessandro Sestini, Florian Fuchs +6
Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards…
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
Florian Fuchs, Jessy Gosselin-Grant, Boris Skuin +5
Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice…
Augmenting Game AI with Deep Reinforcement Learning
Alessandro Sestini, Joakim Bergdahl, Amir Baghi +3
Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters. Producing believable characters, or game AI, remain…
Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre +5
While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting auth…
TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning
Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar +2
In offline reinforcement learning, agents are trained using only a fixed set of stored transitions derived from a source policy. However, this requires that the dataset be labeled…
Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration
Amir Baghi, Jens Sjölund, Joakim Bergdahl +2
Multi-agent reinforcement learning has shown promise in learning cooperative behaviors in team-based environments. However, such methods often demand extensive training time, which…
Improving Conditional Level Generation using Automated Validation in Match-3 Games
Monica Villanueva Aylagas, Joakim Bergdahl, Jonas Gillberg +3
Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the gener…
Reinforcement Learning for High-Level Strategic Control in Tower Defense Games
Joakim Bergdahl, Alessandro Sestini, Linus Gisslén
In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. Many mobile titles feature quick gameplay loops that allow play…
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games
Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini +2
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 th…
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning
Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar +2
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. This paper propo…
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories
Alessandro Sestini, Linus Gisslén, Joakim Bergdahl +2
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosit…
Augmenting Automated Game Testing with Deep Reinforcement Learning
Joakim Bergdahl, Camilo Gordillo, Konrad Tollmar +1
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. Using deep reinforcement…
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents
Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar +1
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 prope…
Adversarial Reinforcement Learning for Procedural Content Generation
Linus Gisslén, Andy Eakins, Camilo Gordillo +2
We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with a…
Imitation Learning with Concurrent Actions in 3D Games
Jack Harmer, Linus Gisslén, Jorge del Val +5
In this work we describe a novel deep reinforcement learning architecture that allows multiple actions to be selected at every time-step in an efficient manner. Multi-action polici…