81,651 research outputs found
Delay and Cooperation in Nonstochastic Bandits
We study networks of communicating learning agents that cooperate to solve a
common nonstochastic bandit problem. Agents use an underlying communication
network to get messages about actions selected by other agents, and drop
messages that took more than hops to arrive, where is a delay
parameter. We introduce \textsc{Exp3-Coop}, a cooperative version of the {\sc
Exp3} algorithm and prove that with actions and agents the average
per-agent regret after rounds is at most of order , where is the
independence number of the -th power of the connected communication graph
. We then show that for any connected graph, for the regret
bound is , strictly better than the minimax regret
for noncooperating agents. More informed choices of lead to bounds which
are arbitrarily close to the full information minimax regret
when is dense. When has sparse components, we show that a variant of
\textsc{Exp3-Coop}, allowing agents to choose their parameters according to
their centrality in , strictly improves the regret. Finally, as a by-product
of our analysis, we provide the first characterization of the minimax regret
for bandit learning with delay.Comment: 30 page
Supervised Quantum Learning without Measurements
We propose a quantum machine learning algorithm for efficiently solving a
class of problems encoded in quantum controlled unitary operations. The central
physical mechanism of the protocol is the iteration of a quantum time-delayed
equation that introduces feedback in the dynamics and eliminates the necessity
of intermediate measurements. The performance of the quantum algorithm is
analyzed by comparing the results obtained in numerical simulations with the
outcome of classical machine learning methods for the same problem. The use of
time-delayed equations enhances the toolbox of the field of quantum machine
learning, which may enable unprecedented applications in quantum technologies
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
The contribution of quality aspects to process control
Process operators often have difficulties with quality supervision and control for the following reasons: (i) analytical results are infrequent and much delayed, (ii) conventional automatic control cannot sufficiently reduce quality deviations, and (iii) several set values can be candidates for correction of quality deviations. Control performance is discussed with regard to these problems, in relation to the degree of buffering, and types of process perturbations and measuring errors. Some methods are discussed for improving the situation, namely, on-line quality estimation from simpler measurements, and integration of off-line quality measurements and on-line quality measurement and estimation by means of state estimators
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