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Robert St-Aubin

dblp:24/2385 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › dynamic programming
approximate dynamic programming		 0.012000
APRICODD: Approximate Policy Construction Using Decision Diagrams · NIPS 2000
Machine learning › Reinforcement learning
markov decision process		 0.012000
APRICODD: Approximate Policy Construction Using Decision Diagrams · NIPS 2000
Machine learning › Reinforcement learning › dynamic programming
value iteration		 0.012000
APRICODD: Approximate Policy Construction Using Decision Diagrams · NIPS 2000

Methods — techniques the papers use, named apart from their topics

algebraic decision diagrams · 0.0
YearPublicationVenuePosition
2000 APRICODD: Approximate Policy Construction Using Decision Diagrams
abstract
We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and policies with much lower time and space requirements than exact dynamic programming. Our method reduces the sizes of the intermediate value functions generated during value iteration by replacing the values at the terminals of the ADD with ranges of values. Our method is demonstrated on a class of large MDPs (with up to 34 billion states), and we compare the results with the optimal value functions.
Robert St-Aubin, Jesse Hoey, Craig Boutilier
 NIPS1
1999 SPUDD: Stochastic Planning using Decision Diagrams
Jesse Hoey, Robert St-Aubin, Alan J. Hu, Craig Boutilier
 UAI2