10,171 research outputs found
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
We consider the problem of multiple agents sensing and acting in environments
with the goal of maximising their shared utility. In these environments, agents
must learn communication protocols in order to share information that is needed
to solve the tasks. By embracing deep neural networks, we are able to
demonstrate end-to-end learning of protocols in complex environments inspired
by communication riddles and multi-agent computer vision problems with partial
observability. We propose two approaches for learning in these domains:
Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning
(DIAL). The former uses deep Q-learning, while the latter exploits the fact
that, during learning, agents can backpropagate error derivatives through
(noisy) communication channels. Hence, this approach uses centralised learning
but decentralised execution. Our experiments introduce new environments for
studying the learning of communication protocols and present a set of
engineering innovations that are essential for success in these domains
Gravitational Model of High Energy Particles in a Collimated Jet
Observations suggest that relativistic particles play a fundamental role in
the dynamics of jets emerging from active galactic nuclei as well as in their
interaction with the intracluster medium. However, no general consensus exists
concerning the acceleration mechanism of those high energy particles. A
gravitational acceleration mechanism is here proposed, in which particles
leaving precise regions within the ergosphere of a rotating supermassive black
hole produce a highly collimated flow. These particles follow unbound geodesics
which are asymptotically parallel to the spin axis of the black hole and are
characterized by the energy , the Carter constant and zero
angular momentum of the component . If environmental effects are
neglected, the present model predicts at distances of about 140 kpc from the
ergosphere the presence of electrons with energies around 9.4 GeV. The present
mechanism can also accelerate protons up to the highest energies observed in
cosmic rays by the present experiments.Comment: 27 pages and 5 figures. Accepted for publication in Astrophysical
Journal. arXiv admin note: text overlap with arXiv:1011.654
The compound Poisson limit ruling periodic extreme behaviour of non-uniformly hyperbolic dynamics
We prove that the distributional limit of the normalised number of returns to
small neighbourhoods of periodic points of non-uniformly hyperbolic dynamical
systems is compound Poisson. The returns to small balls around a fixed point in
the phase space correspond to the occurrence of rare events, or exceedances of
high thresholds, so that there is a connection between the laws of Return Times
Statistics and Extreme Value Laws. The fact that the fixed point in the phase
space is a repelling periodic point implies that there is a tendency for the
exceedances to appear in clusters whose average sizes is given by the Extremal
Index, which depends on the expansion of the system at the periodic point.
We recall that for generic points, the exceedances, in the limit, are
singular and occur at Poisson times. However, around periodic points, the
picture is different: the respective point processes of exceedances converge to
a compound Poisson process, so instead of single exceedances, we have entire
clusters of exceedances occurring at Poisson times with a geometric
distribution ruling its multiplicity.
The systems to which our results apply include: general piecewise expanding
maps of the interval (Rychlik maps), maps with indifferent fixed points
(Manneville-Pomeau maps) and Benedicks-Carleson quadratic maps.Comment: To appear in Communications in Mathematical Physic
Phase Transition and Monopoles Densities in a Nearest Neighbors Two-Dimensional Spin Ice Model
In this work, we show that, due to the alternating orientation of the spins
in the ground state of the artificial square spin ice, the influence of a set
of spins at a certain distance of a reference spin decreases faster than the
expected result for the long range dipolar interaction, justifying the use of
the nearest neighbor two dimensional square spin ice model as an effective
model. Using an extension of the model presented in ref. [Scientific Reports 5,
15875 (2015)], considering the influence of the eight nearest neighbors of each
spin on the lattice, we analyze the thermodynamics of the model and study the
monopoles and string densities dependence as a function of the temperature.Comment: 11 pages, 8 figure
Coalescence Rate of Supermassive Black Hole Binaries Derived from Cosmological Simulations: Detection Rates for LISA and ET
The coalescence history of massive black holes has been derived from
cosmological simulations, in which the evolution of those objects and that of
the host galaxies are followed in a consistent way. The present study indicates
that supermassive black holes having masses greater than underwent up to 500 merger events along their history. The derived
coalescence rate per comoving volume and per mass interval permitted to obtain
an estimate of the expected detection rate distribution of gravitational wave
signals ("ring-down") along frequencies accessible by the planned
interferometers either in space (LISA) or in the ground (Einstein). For LISA,
in its original configuration, a total detection rate of about is
predicted for events having a signal-to-noise ratio equal to 10, expected to
occur mainly in the frequency range . For the Einstein gravitational
wave telescope, one event each 14 months down to one event each 4 years is
expected with a signal-to-noise ratio of 5, occurring mainly in the frequency
interval . The detection of these gravitational signals and their
distribution in frequency would be in the future an important tool able to
discriminate among different scenarios explaining the origin of supermassive
black holes.Comment: 18 pages, 7 figures, to appear in the IJMP
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