Steven J. Bradtke

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4ranked-venue papers

3as first author

0since 2021 · last 1996

—ORCID · none

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

Artificial intelligence and machine learning · 4 · 3 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

3 papers

Planning, search and constraint satisfaction · 45% Reinforcement learning · 40% Motion planning and robot control · 15%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction planning under uncertainty	1	1995	Learning to Act Using Real-Time Dynamic Programming · Artif. Intell. 1995
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty real-time dynamic programming	1	1995	Learning to Act Using Real-Time Dynamic Programming · Artif. Intell. 1995
Machine learning › Reinforcement learning › markov decision process semi-markov decision process	1	1994	Reinforcement Learning Methods for Continuous-Time Markov Decision Problems · NIPS 1994
Machine learning › Reinforcement learning › dynamic programming value iteration	1	1994	Reinforcement Learning Methods for Continuous-Time Markov Decision Problems · NIPS 1994
Robotics › Motion planning and robot control › robot control › optimal control linear quadratic regulator	1	1992	Reinforcement Learning Applied to Linear Quadratic Regulation · NIPS 1992

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

dynamic programming · 0.0stochastic approximation · 0.0q-learning · 0.0TD-learning · 0.0reinforcement learning · 0.0

Year	Publication	Venue	Position
1996	Linear Least-Squares Algorithms for Temporal Difference Learning Steven J. Bradtke, Andrew G. Barto	Mach. Learn.	1
1995	Learning to Act Using Real-Time Dynamic Programming Andrew G. Barto, Steven J. Bradtke, Satinder Singh 0001	Artif. Intell.	2
1994	Reinforcement Learning Methods for Continuous-Time Markov Decision Problems abstract Semi-Markov Decision Problems are continuous time generaliza(cid:173) tions of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approxima(cid:173) tion. Among these are TD(,x), Q-Iearning, and Real-time Dynamic Programming. After reviewing semi-Markov Decision Problems and Bellman's optimality equation in that context, we propose al(cid:173) gorithms similar to those named above, adapted to the solution of semi-Markov Decision Problems. We demonstrate these algorithms by applying them to the problem of determining the optimal con(cid:173) trol for a simple queueing system. We conclude with a discussion of circumstances under which these algorithms may be usefully ap(cid:173) plied. Steven J. Bradtke, Michael O. Duff	NIPS	1
1992	Reinforcement Learning Applied to Linear Quadratic Regulation Steven J. Bradtke	NIPS	1