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» Learning all optimal policies with multiple criteria
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ICML
1994
IEEE
13 years 12 months ago
Markov Games as a Framework for Multi-Agent Reinforcement Learning
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
Michael L. Littman
NIPS
1998
13 years 9 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
PE
2010
Springer
114views Optimization» more  PE 2010»
13 years 6 months ago
Analysis of scheduling policies under correlated job sizes
Correlations in traffic patterns are an important facet of the workloads faced by real systems, and one that has far-reaching consequences on the performance and optimization of t...
Varun Gupta, Michelle Burroughs, Mor Harchol-Balte...
NDSS
2007
IEEE
14 years 2 months ago
Attribute-Based Publishing with Hidden Credentials and Hidden Policies
With Hidden Credentials Alice can send policyencrypted data to Bob in such a way that he can decrypt the data only with the right combination of credentials. Alice gains no knowle...
Apu Kapadia, Patrick P. Tsang, Sean W. Smith
ICML
2009
IEEE
14 years 9 months ago
More generality in efficient multiple kernel learning
Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient desce...
Manik Varma, Bodla Rakesh Babu