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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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
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Markov Decision Processes
This project aimed at finding different ways to improve (SSP-)MDP planners performance when considering computer architectures (e.g., cache-memory, parallelism)
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Markov Decision Processes
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
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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
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