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» Experts in a Markov Decision Process
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NIPS
2008
13 years 9 months ago
Biasing Approximate Dynamic Programming with a Lower Discount Factor
Most algorithms for solving Markov decision processes rely on a discount factor, which ensures their convergence. It is generally assumed that using an artificially low discount f...
Marek Petrik, Bruno Scherrer
NIPS
2007
13 years 9 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
NIPS
2007
13 years 9 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
AAAI
2006
13 years 9 months ago
Incremental Least Squares Policy Iteration for POMDPs
We present a new algorithm, called incremental least squares policy iteration (ILSPI), for finding the infinite-horizon stationary policy for partially observable Markov decision ...
Hui Li, Xuejun Liao, Lawrence Carin
FLAIRS
2004
13 years 9 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber