Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games", Mar 201

Solution and testing of the Abraham-Minkowski controversy in light-atom interacting system

We present the origin of the Abraham-Minkowski controversy of light-matter wave interacting system, which is a special case of the centaury-old Abraham-Minkowski controversy. We solve the controversy of laser-atom interacting case and find that for systems with perfect atomic spatial coherence, the systems prefer to show Minkowski momentum and canonical momentum for the atoms and light, respectively; while for the systems where the atoms are spatially incoherent, the momenta of light and atoms would choose the Abraham and kinetic forms. The provement of our solution can be realized with current techniques, using three-dimensional optical lattices and electromagnetically-induced absorption (EIA) to distinguish the kinetic and canonical recoil momentum of ultra-cold atoms.Comment: 4 pages, 3 figure

Foreclosure of Securitized Commercial Mortgages - A Model of the Special Servicer

The decision to foreclose on a CMBS mortgage is made by the special servicer. A mortgage loan is in special servicing when it is either delinquent or in a state of imminent default. A special servicer should represent the interests of the underlying CMBS bondholders by returning the highest possible value to the investors. In this paper, we show that a special servicer\u27s compensation structure results in an incentive for her to extend a loan beyond the time desired by its bondholders. We develop a model and demonstrate how compensation incentives interact and influence a special servicer\u27s foreclosure decisions. Our model takes into consideration the dynamic nature of such a decision by viewing is as a dynamic programming problem whereby foreclosure represents a discrete terminal state of an optimal stopping problem. This model thus captures the trade-off between continuation of a loan and termination and we use this model to determine how the stopping rule changes under various compensation structures

The spin alignment of galaxies with the large-scale tidal field in hydrodynamic simulations

The correlation between the spins of dark matter halos and the large-scale structure (LSS) has been studied in great detail over a large redshift range, while investigations of galaxies are still incomplete. Motivated by this point, we use the state-of-the-art hydrodynamic simulation, Illustris-1, to investigate mainly the spin--LSS correlation of galaxies at redshift of $z=0$. We mainly find that the spins of low-mass, blue, oblate galaxies are preferentially aligned with the slowest collapsing direction ($e_3$) of the large-scale tidal field, while massive, red, prolate galaxy spins tend to be perpendicular to $e_3$. The transition from a parallel to a perpendicular trend occurs at $\sim10^{9.4} M_{\odot}/h$ in the stellar mass, $\sim0.62$ in the g-r color, and $\sim0.4$ in triaxiality. The transition stellar mass decreases with increasing redshifts. The alignment was found to be primarily correlated with the galaxy stellar mass. Our results are consistent with previous studies both in N-body simulations and observations. Our study also fills the vacancy in the study of the galaxy spin--LSS correlation at $z=0$ using hydrodynamical simulations and also provides important insight to understand the formation and evolution of galaxy angular momentum.Comment: 9 pages, 6 figures, 1 table. Accepted for publication in ApJ, match the proof versio

Disentangled Variational Auto-Encoder for Semi-supervised Learning

Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning. The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE. Given the limited labeled data, learning the parameters for the classifiers may not be an optimal solution for exploiting label information. Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.Comment: 6 figures, 10 pages, Information Sciences 201