Portrait de Hugo Delhaye n'est pas disponible

Hugo Delhaye

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Lune Bellec
Co-supervisor
Audrey Durand

Publications

Human-AI Alignment of Learning Trajectories in Video Games: a continual RL benchmark proposal
Yann Harel
Lune Bellec
François Paugam
Hugo Delhaye
Audrey Durand
We propose a design for a continual reinforcement learning (CRL) benchmark called GHAIA, centered on human-AI alignment of learning trajecto… (voir plus)ries in structured video game environments. Using \textit{Super Mario Bros.} as a case study, gameplay is decomposed into short, annotated scenes organized into diverse task sequences based on gameplay patterns and difficulty. Evaluation protocols measure both plasticity and stability, with flexible revisit and pacing schedules. A key innovation is the inclusion of high-resolution human gameplay data collected under controlled conditions, enabling direct comparison of human and agent learning. In addition to adapting classical CRL metrics like forgetting and backward transfer, we introduce semantic transfer metrics capturing learning over groups of scenes sharing similar game patterns. We demonstrate the feasibility of our approach on human and agent data, and discuss key aspects of the first release for community input.
2025-06-20
rl-conference.cc/RLC/2025/Workshop/RLVG (publié)
openreview.net
openreview.net
Human-AI Alignment of Learning Trajectories in Video Games: a continual RL benchmark proposal
Yann Harel
Lune Bellec
François Paugam
Hugo Delhaye
Audrey Durand
We propose a design for a continual reinforcement learning (CRL) benchmark called GHAIA, centered on human-AI alignment of learning trajecto… (voir plus)ries in structured video game environments. Using \textit{Super Mario Bros.} as a case study, gameplay is decomposed into short, annotated scenes organized into diverse task sequences based on gameplay patterns and difficulty. Evaluation protocols measure both plasticity and stability, with flexible revisit and pacing schedules. A key innovation is the inclusion of high-resolution human gameplay data collected under controlled conditions, enabling direct comparison of human and agent learning. In addition to adapting classical CRL metrics like forgetting and backward transfer, we introduce semantic transfer metrics capturing learning over groups of scenes sharing similar game patterns. We demonstrate the feasibility of our approach on human and agent data, and discuss key aspects of the first release for community input.
2025-06-20
rl-conference.cc/RLC/2025/Workshop/RLVG (accepté)
openreview.net
openreview.net
Are vividness judgments in mental imagery correlated with perceptual thresholds?
Ian Charest
Clémence Bertrand Pilon
Hugo Delhaye
Vincent Taschereau-Dumouchel
Frédéric Gosselin
2023-08-01
Journal of Vision (publié)
doi.org