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IJCAI
2007
13 years 9 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
AAAI
1998
13 years 9 months ago
Solving Very Large Weakly Coupled Markov Decision Processes
We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
ATAL
2007
Springer
14 years 1 months ago
On opportunistic techniques for solving decentralized Markov decision processes with temporal constraints
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve...
Janusz Marecki, Milind Tambe
DSN
2006
IEEE
14 years 1 months ago
Automatic Recovery Using Bounded Partially Observable Markov Decision Processes
This paper provides a technique, based on partially observable Markov decision processes (POMDPs), for building automatic recovery controllers to guide distributed system recovery...
Kaustubh R. Joshi, William H. Sanders, Matti A. Hi...
ICC
2007
IEEE
137views Communications» more  ICC 2007»
14 years 2 months ago
Optimality and Complexity of Opportunistic Spectrum Access: A Truncated Markov Decision Process Formulation
— We consider opportunistic spectrum access (OSA) which allows secondary users to identify and exploit instantaneous spectrum opportunities resulting from the bursty traffic of ...
Dejan V. Djonin, Qing Zhao, Vikram Krishnamurthy