5 citations · 17 across the 26 of their papers we have counts for
5 papers · 1 filter
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…