5 citations · 17 across the 26 of their papers we have counts for
26 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…
SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
Carlo Romeo, Girolamo Macaluso, Alessandro Sestini +1
Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computational costs and long, multi-stage t…
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…
Self-correcting Reward Shaping via Language Models for Reinforcement Learning Agents in Games
António Afonso, Iolanda Leite, Alessandro Sestini +3
Reinforcement Learning (RL) in games has gained significant momentum in recent years, enabling the creation of different agent behaviors that can transform a player's gaming experi…
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…
Real-Time Diffusion Policies for Games: Enhancing Consistency Policies with Q-Ensembles
Ruoqi Zhang, Ziwei Luo, Jens Sjölund +3
Diffusion models have shown impressive performance in capturing complex and multi-modal action distributions for game agents, but their slow inference speed prevents practical depl…
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…
SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning
Carlo Romeo, Girolamo Macaluso, Alessandro Sestini +1
High update-to-data (UTD) ratio algorithms in reinforcement learning (RL) improve sample efficiency but incur high computational costs, limiting real-world scalability. We propose…
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…
A Benchmark Environment for Offline Reinforcement Learning in Racing Games
Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov
Offline Reinforcement Learning (ORL) is a promising approach to reduce the high sample complexity of traditional Reinforcement Learning (RL) by eliminating the need for continuous…
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…
Leveraging Large Language Models for Efficient Failure Analysis in Game Development
Leonardo Marini, Linus Gisslén, Alessandro Sestini
In games, and more generally in the field of software development, early detection of bugs is vital to maintain a high quality of the final product. Automated tests are a powerful…
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation
Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov
Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as effective initialization for online algorithms, enhancing sample e…
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning
Derek Yadgaroff, Alessandro Sestini, Konrad Tollmar +2
Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production. However, generalization - the ability to perform well…
Generating Personas for Games with Multimodal Adversarial Imitation Learning
William Ahlberg, Alessandro Sestini, Konrad Tollmar +1
Reinforcement learning has been widely successful in producing agents capable of playing games at a human level. However, this requires complex reward engineering, and the agent's…
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…
Efficient Ground Vehicle Path Following in Game AI
Rodrigue de Schaetzen, Alessandro Sestini
This short paper presents an efficient path following solution for ground vehicles tailored to game AI. Our focus is on adapting established techniques to design simple solutions w…
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…
Contextual Decision Trees
Tommaso Aldinucci, Enrico Civitelli, Leonardo di Gangi +1
Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble. The tr…
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…
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
In this article we study the problem of training intelligent agents using Reinforcement Learning for the purpose of game development. Unlike systems built to replace human players…
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environm…