Jaël Champagne Gareau
Jaël Champagne Gareau
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MDP
Towards Topologically Diverse Probabilistic Planning Benchmarks
Markov Decision Processes (MDPs) are often used in Artificial Intelligence to solve probabilistic sequential decision-making problems. …
Jaël Champagne Gareau
,
Éric Beaudry
,
Vladimir Makarenkov
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Citation
Code
Projet
Diapositives
Cache-Efficient Dynamic Programming MDP Solver
Automated planning research often focuses on developing new algorithms to improve the computational performance of planners, but …
Jaël Champagne Gareau
,
Guillaume Gosset
,
Éric Beaudry
,
Vladimir Makarenkov
PDF
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Code
Projet
Poster
Diapositives
DOI
Supplementary Material
Processus Décisionnels de Markov
Ce projet vise à trouver différentes façons d’améliorer la performance des planificateurs de (SSP-)MDP en considérant l’architecture des ordinateurs (p.ex., la mémoire cache, le parallélisme, etc.).
Code
Cache-Efficient Memory Representation of Markov Decision Processes
Research in automated planning typically focuses on the development of new or improved algorithms. Yet, an equally important but often …
Jaël Champagne Gareau
,
Éric Beaudry
,
Vladimir Makarenkov
PDF
Citation
Projet
Diapositives
DOI
pcTVI: Parallel MDP Solver Using a Decomposition Into Independent Chains
Markov Decision Processes (MDPs) are useful to solve real-world probabilistic planning problems. However, finding an optimal solution …
Jaël Champagne Gareau
,
Éric Beaudry
,
Vladimir Makarenkov
PDF
Citation
Projet
Diapositives
DOI
Citation
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