M
Michael L. Littman
Researcher at Brown University
Publications - 336
Citations - 46236
Michael L. Littman is an academic researcher from Brown University. The author has contributed to research in topics: Reinforcement learning & Markov decision process. The author has an hindex of 78, co-authored 323 publications receiving 41859 citations. Previous affiliations of Michael L. Littman include Telcordia Technologies & AT&T.
Papers
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Proceedings Article
Activity recognition from accelerometer data
TL;DR: This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Proceedings Article
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
TL;DR: In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route efficiently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically.
Proceedings Article
Acting Optimally in Partially Observable Stochastic Domains
TL;DR: The existing algorithms for computing optimal control strategies for partially observable stochastic environments are found to be highly computationally inefficient and a new algorithm is developed that is empirically more efficient.
Proceedings Article
Graphical models for game theory
TL;DR: The main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs.
Book ChapterDOI
Learning policies for partially observable environments: scaling up
TL;DR: This paper discusses several simple solution methods and shows that all are capable of finding near- optimal policies for a selection of extremely small POMDP'S taken from the learning literature, but shows that none are able to solve a slightly larger and noisier problem based on robot navigation.