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NIPS
2007
13 years 6 months ago
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
Ambuj Tewari, Peter L. Bartlett
IAT
2009
IEEE
13 years 9 months ago
Offline Planning for Communication by Exploiting Structured Interactions in Decentralized MDPs
Variants of the decentralized MDP model focus on problems exhibiting some special structure that makes them easier to solve in practice. Our work is concerned with two main issues...
Hala Mostafa, Victor R. Lesser
AI
2000
Springer
13 years 5 months ago
Stochastic dynamic programming with factored representations
Markov decisionprocesses(MDPs) haveproven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, stat...
Craig Boutilier, Richard Dearden, Moisés Go...
AAAI
1997
13 years 6 months ago
Model Minimization in Markov Decision Processes
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for so...
Thomas Dean, Robert Givan
ATAL
2008
Springer
13 years 7 months ago
Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies
Decentralized Markov decision processes are frequently used to model cooperative multi-agent systems. In this paper, we identify a subclass of general DEC-MDPs that features regul...
Thomas Gabel, Martin A. Riedmiller