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