948 research outputs found
On utilizing weak estimators to achieve the online classification of data streams
Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio
Statistical Computations Underlying the Dynamics of Memory Updating
Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861
Advancing the Applicability of Reinforcement Learning to Autonomous Control
ï»żMit dateneffizientem Reinforcement Learning (RL) konnten
beeindruckendeErgebnisse erzielt werden, z.B. fĂŒr die Regelung von
Gasturbinen. In derPraxis erfordert die Anwendung von RL jedoch noch viel
manuelle Arbeit, wasbisher RL fĂŒr die autonome Regelung untauglich
erscheinen lieĂ. Dievorliegende Arbeit adressiert einige der verbleibenden
Probleme, insbesonderein Bezug auf die ZuverlÀssigkeit der
Policy-Erstellung.
Es werden zunÀchst RL-Probleme mit diskreten Zustands- und
AktionsrĂ€umenbetrachtet. FĂŒr solche Probleme wird hĂ€ufig ein MDP aus
BeobachtungengeschÀtzt, um dann auf Basis dieser MDP-SchÀtzung eine Policy
abzuleiten. DieArbeit beschreibt, wie die SchÀtzer-Unsicherheit des MDP in
diePolicy-Erstellung eingebracht werden kann, um mit diesem Wissen das
Risikoeiner schlechten Policy aufgrund einer fehlerhaften MDP-SchÀtzung
zuverringern. AuĂerdem wird so effiziente Exploration sowie
Policy-Bewertungermöglicht.
AnschlieĂend wendet sich die Arbeit Problemen mit
kontinuierlichenZustandsrÀumen zu und konzentriert sich auf auf
RL-Verfahren, welche aufFitted Q-Iteration (FQI) basieren, insbesondere
Neural Fitted Q-Iteration(NFQ). Zwar ist NFQ sehr dateneffizient, jedoch
nicht so zuverlĂ€ssig, wie fĂŒrdie autonome Regelung nötig wĂ€re. Die Arbeit
schlÀgt die Verwendung vonEnsembles vor, um die ZuverlÀssigkeit von NFQ zu
erhöhen. Es werden eine Reihevon Möglichkeiten der Ensemble-Nutzung
entworfen und evaluiert. Bei allenbetrachteten RL-Problemen sorgen
Ensembles fĂŒr eine zuverlĂ€ssigere Erstellungguter Policies.
Im nÀchsten Schritt werden Möglichkeiten der Policy-Bewertung
beikontinuierlichen ZustandsrÀumen besprochen. Die Arbeit schlÀgt vor,
FittedPolicy Evaluation (FPE), eine Variante von FQI fĂŒr Policy Evaluation,
mitanderen Regressionsverfahren und/oder anderen DatensÀtzen zu
kombinieren, umein MaĂ fĂŒr die Policy-QualitĂ€t zu erhalten. Experimente
zeigen, dassExtra-Tree-FPE ein realistisches QualitĂ€tsmaĂ fĂŒr
NFQ-generierte Policies liefernkann.
SchlieĂlich kombiniert die Arbeit Ensembles und Policy-Bewertung, um mit
sichÀndernden RL-Problemen umzugehen. Der wesentliche Beitrag ist das
EvolvingEnsemble, dessen Policy sich langsam Àndert, indem alte,
untaugliche Policiesentfernt und neue hinzugefĂŒgt werden. Es zeigt sich,
dass das EvolvingEnsemble deutlich besser funktioniert als einfachere
AnsÀtze.With data-efficient reinforcement learning (RL) methods impressive
resultscould be achieved, e.g., in the context of gas turbine control.
However, inpractice the application of RL still requires much human
intervention, whichhinders the application of RL to autonomous control.
This thesis addressessome of the remaining problems, particularly regarding
the reliability of thepolicy generation process.
The thesis first discusses RL problems with discrete state and action
spaces.In that context, often an MDP is estimated from observations. It is
describedhow to incorporate the estimators' uncertainties into the policy
generationprocess. This information can then be used to reduce the risk of
obtaining apoor policy due to flawed MDP estimates. Moreover, it is
discussed how to usethe knowledge of uncertainty for efficient exploration
and the assessment ofpolicy quality without requiring the policy's
execution.
The thesis then moves on to continuous state problems and focuses on
methodsbased on fitted Q-iteration (FQI), particularly neural fitted
Q-iteration(NFQ). Although NFQ has proven to be very data-efficient, it is
not asreliable as required for autonomous control. The thesis proposes to
useensembles to increase reliability. Several ways of ensemble usage in an
NFQcontext are discussed and evaluated on a number of benchmark domains. It
showsthat in all considered domains with ensembles good policies can be
producedmore reliably.
Next, policy assessment in continuous domains is discussed. The
thesisproposes to use fitted policy evaluation (FPE), an adaptation of FQI
to policyevaluation, combined with a different function approximator and/or
differentdataset to obtain a measure for policy quality. Results of
experiments showthat extra-tree FPE, applied to policies generated by NFQ,
produces valuefunctions that can well be used to reason about the true
policy quality.
Finally, the thesis combines ensembles and policy assessment to derive
methodsthat can deal with changing environments. The major contribution is
theevolving ensemble. The policy of the evolving ensemble changes slowly as
newpolicies are added and old policies removed. It turns out that the
evolvingensemble approaches work considerably better than simpler
approaches likesingle policies learned with recent observations or simple
ensembles
Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments
Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201
Escapist policy rules
We study a simple, microfounded macroeconomic system in which the monetary authority employs a Taylor-type policy rule. We analyze situations in which the self-confirming equilibrium is unique and learnable according to Bullard and Mitra (2002). We explore the prospects for the use of 'large deviation' theory in this context, as employed by Sargent (1999) and Cho, Williams, and Sargent (2002). We show that our system can sometimes depart from the self-confirming equilibrium towards a non-equilibrium outcome characterized by persistently low nominal interest rates and persistently low inflation. Thus we generate events that have some of the properties of "liquidity traps" observed in the data, even though the policymaker remains committed to a Taylor-type policy rule which otherwise has desirable stabilization properties
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