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

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 $\sim 10^{9} M_{\odot}$ 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 $15 yr^{-1}$ is predicted for events having a signal-to-noise ratio equal to 10, expected to occur mainly in the frequency range $4-9 mHz$. 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 $10-20 Hz$. 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

Gravitational Waves from Wobbling Pulsars

The prospects for detection of gravitational waves from precessing pulsars have been considered by constructing fully relativistic rotating neutron star models and evaluating the expected wave amplitude $h$ from a galactic source. For a "typical" neutron matter equation of state and observed rotation rates, it is shown that moderate wobble angles may render an observable signal from a nearby source once the present generation of interferometric antennas becomes operative.Comment: PlainTex, 7 pp. , no figures, IAG/USP Rep. 6

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 $E$, the Carter constant ${\cal Q}$ and zero angular momentum of the component $L_z$. 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

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

Are Neutron-Rich Elements Produced in the Collapse of Strange Dwarfs ?

The structure of strange dwarfs and that of hybrid stars with same baryonic number is compared. There is a critical mass (M~0.24M_sun) in the strange dwarf branch, below which configurations with the same baryonic number in the hybrid star branch are more stable. If a transition occurs between both branches, the collapse releases an energy of about of 3x10^{50} erg, mostly under the form of neutrinos resulting from the conversion of hadronic matter onto strange quark matter. Only a fraction (~4%) is required to expel the outer neutron-rich layers. These events may contribute significantly to the chemical yield of nuclides with A>80 in the Galaxy, if their frequency is of about one per 1500 years.Comment: Accepted for publication in IJMP

Ajuste de modelos não lineares a dados de crescimento de suínos.

Modelos matemáticos não lineares. Ajuste de modelos não lineares a dados de crescimento de suínos