4,917 research outputs found
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
Distributed Linear Parameter Estimation: Asymptotically Efficient Adaptive Strategies
The paper considers the problem of distributed adaptive linear parameter
estimation in multi-agent inference networks. Local sensing model information
is only partially available at the agents and inter-agent communication is
assumed to be unpredictable. The paper develops a generic mixed time-scale
stochastic procedure consisting of simultaneous distributed learning and
estimation, in which the agents adaptively assess their relative observation
quality over time and fuse the innovations accordingly. Under rather weak
assumptions on the statistical model and the inter-agent communication, it is
shown that, by properly tuning the consensus potential with respect to the
innovation potential, the asymptotic information rate loss incurred in the
learning process may be made negligible. As such, it is shown that the agent
estimates are asymptotically efficient, in that their asymptotic covariance
coincides with that of a centralized estimator (the inverse of the centralized
Fisher information rate for Gaussian systems) with perfect global model
information and having access to all observations at all times. The proof
techniques are mainly based on convergence arguments for non-Markovian mixed
time scale stochastic approximation procedures. Several approximation results
developed in the process are of independent interest.Comment: Submitted to SIAM Journal on Control and Optimization journal.
Initial Submission: Sept. 2011. Revised: Aug. 201
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics
This paper focuses on the problem of recursive nonlinear least squares
parameter estimation in multi-agent networks, in which the individual agents
observe sequentially over time an independent and identically distributed
(i.i.d.) time-series consisting of a nonlinear function of the true but unknown
parameter corrupted by noise. A distributed recursive estimator of the
\emph{consensus} + \emph{innovations} type, namely , is
proposed, in which the agents update their parameter estimates at each
observation sampling epoch in a collaborative way by simultaneously processing
the latest locally sensed information~(\emph{innovations}) and the parameter
estimates from other agents~(\emph{consensus}) in the local neighborhood
conforming to a pre-specified inter-agent communication topology. Under rather
weak conditions on the connectivity of the inter-agent communication and a
\emph{global observability} criterion, it is shown that at every network agent,
the proposed algorithm leads to consistent parameter estimates. Furthermore,
under standard smoothness assumptions on the local observation functions, the
distributed estimator is shown to yield order-optimal convergence rates, i.e.,
as far as the order of pathwise convergence is concerned, the local parameter
estimates at each agent are as good as the optimal centralized nonlinear least
squares estimator which would require access to all the observations across all
the agents at all times. In order to benchmark the performance of the proposed
distributed estimator with that of the centralized nonlinear
least squares estimator, the asymptotic normality of the estimate sequence is
established and the asymptotic covariance of the distributed estimator is
evaluated. Finally, simulation results are presented which illustrate and
verify the analytical findings.Comment: 28 pages. Initial Submission: Feb. 2016, Revised: July 2016,
Accepted: September 2016, To appear in IEEE Transactions on Signal and
Information Processing over Networks: Special Issue on Inference and Learning
over Network
Bloch-like oscillations in a one-dimensional lattice with long-range correlated disorder
We study the dynamics of an electron subjected to a uniform electric field
within a tight-binding model with long-range-correlated diagonal disorder. The
random distribution of site energies is assumed to have a power spectrum with . Moura and Lyra [Phys. Rev. Lett. {\bf
81}, 3735 (1998)] predicted that this model supports a phase of delocalized
states at the band center, separated from localized states by two mobility
edges, provided . We find clear signatures of Bloch-like
oscillations of an initial Gaussian wave packet between the two mobility edges
and determine the bandwidth of extended states, in perfect agreement with the
zero-field prediction.Comment: 4 pages, 5 figure
Critical wave-packet dynamics in the power-law bond disordered Anderson Model
We investigate the wave-packet dynamics of the power-law bond disordered
one-dimensional Anderson model with hopping amplitudes decreasing as
. We consider the critical case ().
Using an exact diagonalization scheme on finite chains, we compute the
participation moments of all stationary energy eigenstates as well as the
spreading of an initially localized wave-packet. The eigenstates
multifractality is characterized by the set of fractal dimensions of the
participation moments. The wave-packet shows a diffusive-like spread developing
a power-law tail and achieves a stationary non-uniform profile after reflecting
at the chain boundaries. As a consequence, the time-dependent participation
moments exhibit two distinct scaling regimes. We formulate a finite-size
scaling hypothesis for the participation moments relating their scaling
exponents to the ones governing the return probability and wave-function
power-law decays
Priorização de genes candidatos utilizando mineração de textos.
O objetivo deste trabalho foi desenvolver e/ou adaptar metodologias de mineração de textos para identificar genes candidatos relacionados a alguma característica fenotípica de interesse econômico para a agricultura brasileira.CIIC 2013. No 13607
Bloch oscillations in an aperiodic one-dimensional potential
We study the dynamics of an electron subjected to a static uniform electric
field within a one-dimensional tight-binding model with a slowly varying
aperiodic potential. The unbiased model is known to support phases of localized
and extended one-electron states separated by two mobility edges. We show that
the electric field promotes sustained Bloch oscillations of an initial Gaussian
wave packet whose amplitude reflects the band width of extended states. The
frequency of these oscillations exhibit unique features, such as a sensitivity
to the initial wave packet position and a multimode structure for weak fields,
originating from the characteristics of the underlying aperiodic potential.Comment: 6 pages, 7 figure
Desempenho de cultivares de arroz de terras altas sob plantio direto e convencional.
Para avaliar a viabilidade de uso do plantio direto em arroz (Oryza sativa L.) de terras altas, bem como o comportamento das novas cultivares nesse sistema de cultivo, experimentos foram conduzidos em Santa Helena de Goiás (GO), Brasil, em um Latossolo Vermelho-Escuro, de uso contínuo sob plantio direto há 14 anos. Foram testadas 14 cultivares de arroz de terras altas sob plantio direto e convencional em duas safras (1998/99 e 1999/2000), utilizando delineamento de blocos casualizados, com quatro repetições. As parcelas constituíram-se de quatro linhas de 5 m, espaçadas de 0,4 m entre si. As variáveis avaliadas foram: produtividade de grãos, altura de plantas, florescimento, acamamento e incidência de doenças. Pelos resultados obtidos, verificaram-se altas produtividades de grãos das cultivares, as quais apresentam diferenças no desempenho em diferentes anos, porém semelhantes nos sistemas de plantio. Em condições de menor disponibilidade hídrica e na ausência de adubação na semeadura, o plantio direto proporciona rendimento de grãos igual ao plantio convencional
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