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dc.contributor.author
Charypar, David
dc.contributor.author
Nagel, Kai
dc.date.accessioned
2017-11-06T08:51:56Z
dc.date.available
2017-06-09T05:52:49Z
dc.date.available
2017-11-03T13:42:01Z
dc.date.available
2017-11-06T08:51:56Z
dc.date.issued
2005
dc.identifier.issn
0361-1981
dc.identifier.issn
2169-4052
dc.identifier.other
10.3141/1935-19
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/23511
dc.identifier.doi
10.3929/ethz-b-000023511
dc.description.abstract
Q-learning is a method from artificial intelligence to solve the reinforcement learning problem (RLP), defined as follows. An agent is faced with a set of states, S. For each state s there is a set of actions, A(s), that the agent can take and that takes the agent (deterministically or stochastically) to another state. For each state the agent receives a (possibly stochastic) reward. The task is to select actions such that the reward is maximized. Activity generation is for demand generation in the context of transportation simulation. For each member of a synthetic population, a daily activity plan stating a sequence of activities (e.g., home-work-shop-home), including locations and times, needs to be found. Activities at different locations generate demand for transportation. Activity generation can be modeled as an RLP with the states given by the triple (type of activity, starting time of activity, time already spent at activity). The possible actions are either to stay at a given activity or to move to another activity. Rewards are given as "utility per time slice," which corresponds to a coarse version of marginal utility. Q-learning has the property that, by repeating similar experiences over and over again, the agent looks forward in time; that is, the agent can also go on paths through state space in which high rewards are given only at the end. This paper presents computational results with such an algorithm for daily activity planning.
en_US
dc.language.iso
en
en_US
dc.publisher
Transportation Research Board (TRB)
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Q-learning for flexible learning of daily activity plans
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.journal.title
Transportation Research Record
ethz.journal.volume
1935
en_US
ethz.journal.abbreviated
Transp. res. rec.
ethz.pages.start
163
en_US
ethz.pages.end
169
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
000024408
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02655 - Netzwerk Stadt und Landschaft D-ARCH::02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02655 - Netzwerk Stadt und Landschaft D-ARCH
*
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
ethz.date.deposited
2017-06-09T05:52:55Z
ethz.source
ECIT
ethz.identifier.importid
imp59364d15564e049436
ethz.ecitpid
pub:38569
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T10:57:34Z
ethz.rosetta.lastUpdated
2019-02-02T13:13:41Z
ethz.rosetta.versionExported
true
ethz.COinS
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