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NIPS
1998
13 years 10 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
NIPS
1996
13 years 10 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
BMCBI
2008
116views more  BMCBI 2008»
13 years 8 months ago
IgTM: An algorithm to predict transmembrane domains and topology in proteins
Background: Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI...
Piedachu Peris, Damián López, Marcel...
CORR
2008
Springer
122views Education» more  CORR 2008»
13 years 8 months ago
Strategy Improvement for Concurrent Safety Games
We consider concurrent games played on graphs. At every round of the game, each player simultaneously and independently selects a move; the moves jointly determine the transition ...
Krishnendu Chatterjee, Luca de Alfaro, Thomas A. H...
CSL
2010
Springer
13 years 8 months ago
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on...
Blaise Thomson, Steve Young