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» Markov Decision Processes with Arbitrary Reward Processes
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JMLR
2008
129views more  JMLR 2008»
15 years 4 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
144
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ICASSP
2010
IEEE
15 years 4 months ago
Distributed learning in cognitive radio networks: Multi-armed bandit with distributed multiple players
—We consider a cognitive radio network with distributed multiple secondary users, where each user independently searches for spectrum opportunities in multiple channels without e...
Keqin Liu, Qing Zhao
ICC
2007
IEEE
121views Communications» more  ICC 2007»
15 years 10 months ago
Structure and Optimality of Myopic Sensing for Opportunistic Spectrum Access
We consider opportunistic spectrum access for secondary users over multiple channels whose occupancy by primary users is modeled as discrete-time Markov processes. Due to hardware...
Qing Zhao, Bhaskar Krishnamachari
ICML
2006
IEEE
16 years 5 months ago
PAC model-free reinforcement learning
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...
NIPS
2004
15 years 5 months ago
A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees
We introduce a new algorithm based on linear programming that approximates the differential value function of an average-cost Markov decision process via a linear combination of p...
Daniela Pucci de Farias, Benjamin Van Roy