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» MDPs: Learning in Varying Environments
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ICML
2003
IEEE
14 years 10 months ago
Planning in the Presence of Cost Functions Controlled by an Adversary
We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a rob...
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum
JMLR
2008
129views more  JMLR 2008»
13 years 9 months ago
Finite-Time Bounds for Fitted Value Iteration
In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decisi...
Rémi Munos, Csaba Szepesvári
AMAI
2004
Springer
14 years 3 months ago
Learning via Finitely Many Queries
This work introduces a new query inference model that can access data and communicate with a teacher by asking finitely many boolean queries in a language L. In this model the pa...
Andrew C. Lee
IADIS
2003
13 years 11 months ago
Knowledge Acquisition Strategies and Navigation in Hypermedia Learning Environments: THe Influence of Instructional Design Prope
In order to understand and enhance the value of new media in education it is necessary to develop criteria for the evaluation of the effectiveness of learning with hypermedia envi...
Mattias Steinke, Thomas Huk, Christian Floto
ICPR
2008
IEEE
14 years 11 months ago
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...