771 search results - page 7 / 155 » Markov Decision Processes with Arbitrary Reward Processes |
ICML
2004
IEEE
16 years 15 days ago
2004
IEEE
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
ICML
2006
IEEE
16 years 15 days ago
2006
IEEE
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
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IROS
2009
IEEE
15 years 6 months ago
2009
IEEE
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
ML
2002
ACM
14 years 11 months ago
2002
ACM
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
AAAI
1997
15 years 1 months ago
1997
Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dy...