Skip to content

Reinforcement Learning with History Lists Solving Partially Observable Decision Processes byUsing Short Term Memory

Spend $50 to get a free movie!

ISBN-10: 3838106210

ISBN-13: 9783838106212

Edition: 2009

Authors: Stephan Timmer

List price: $102.00
Blue ribbon 30 day, 100% satisfaction guarantee!
Out of stock
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!


A very general framework for modeling uncertainty in learning environments is given by Partially observable Markov Decision Processes (POMDPs). In a POMDP setting, the learning agent infers a policy for acting optimally in all possible states of the environment, while receiving only observations of these states. The basic idea for coping with partial observability is to include memory into the representation of the policy. Perfect memory is provided by the belief space, i.e. the space of probability distributions over environmental states. However, computing policies defined on the belief space requires a considerable amount of prior knowledge about the learning problem and is expensive in…    
Customers also bought

Book details

List price: $102.00
Copyright year: 2009
Publisher: S�dwestdeutscher Verlag f�r Hochschulschriften AG & Company KG
Binding: Paperback
Pages: 160
Size: 6.00" wide x 9.00" long x 0.34" tall
Weight: 0.506