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A (Revised) Survey of Approximate Methods for Solving Partially Observable Markov Decision
 

Summary: A (Revised) Survey of Approximate Methods for
Solving Partially Observable Markov Decision
Processes
Douglas Aberdeen
National ICT Australia,
Canberra, Australia.
December 8, 2003
Abstract
Partially observable Markov decision processes (POMDPs) are inter-
esting because they provide a general framework for learning in the pres-
ence of multiple forms of uncertainty. We survey methods for learning
within the POMDP framework. Because exact methods are intractable
we concentrate on approximate methods. We explore two versions of the
POMDP training problem: learning when a model of the POMDP is
known, and the much harder problem of learning when a model is not
available. The methods used to solve POMDPs are sometimes referred to
as reinforcement learning algorithms because the only feedback provided
to the agent is a scalar reward signal at each time step.
1 Introduction
Partially observable Markov decision processes (POMDPs) provide a framework

  

Source: Aberdeen, Douglas - National ICT Australia & Computer Sciences Laboratory, Australian National University

 

Collections: Computer Technologies and Information Sciences