The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

The Structure of Barium in the hcp Phase Under High Pressure

Recent experimental results on two hcp phases of barium under high pressure show interesting variation of the lattice parameters. They are here interpreted in terms of electronic structure calculation by using the LMTO method and generalized pseudopotential theory (GPT) with a NFE-TBB approach. In phase II the dramatic drop in c/a is an instability analogous to that in the group II metals but with the transfer of s to d electrons playing a crucial role in Ba. Meanwhile in phase V, the instability decrease a lot due to the core repulsion at very high pressure. PACS numbers: 62.50+p, 61.66Bi, 71.15.Ap, 71.15Hx, 71.15LaComment: 29 pages, 8 figure

Electronic Structure of New LiFeAs High-Tc Superconductor

We present results of it ab initio LDA calculations of electronic structure of "next generation" layered ironpnictide High-Tc superconductor LiFeAs (Tc=18K). Obtained electronic structure of LiFeAs is very similar to recently studied ReOFeAs (Re=La,Ce,Pr,Nd,Sm) and AFe2As2 (A=Ba,Sr) compounds. Namely close to the Fermi level its electronic properties are also determined ma inly by Fe 3d-orbitals of FeAs4 two-dimensional layers. Band dispersions of LiFeAs are very similar to the LaOFeAs and BaFe2As2 systems as well as the shape of the Fe-3d density o f states and Fermi surface.Comment: 4 pages, 5 figures; Electronic structure improved with respect to new experimental crystal structure dat

Approximation of corner polyhedra with families of intersection cuts

We study the problem of approximating the corner polyhedron using intersection cuts derived from families of lattice-free sets in $\mathbb{R}^n$. In particular, we look at the problem of characterizing families that approximate the corner polyhedron up to a constant factor, which depends only on $n$ and not the data or dimension of the corner polyhedron. The literature already contains several results in this direction. In this paper, we use the maximum number of facets of lattice-free sets in a family as a measure of its complexity and precisely characterize the level of complexity of a family required for constant factor approximations. As one of the main results, we show that, for each natural number $n$, a corner polyhedron with $n$ basic integer variables and an arbitrary number of continuous non-basic variables is approximated up to a constant factor by intersection cuts from lattice-free sets with at most $i$ facets if $i> 2^{n-1}$ and that no such approximation is possible if $i \leq 2^{n-1}$. When the approximation factor is allowed to depend on the denominator of the fractional vertex of the linear relaxation of the corner polyhedron, we show that the threshold is $i > n$ versus $i \leq n$. The tools introduced for proving such results are of independent interest for studying intersection cuts

Adequacy of Approximations in GW Theory

We use an all-electron implementation of the GW approximation to analyze several possible sources of error in the theory and its implementation. Among these are convergence in the polarization and Green's functions, the dependence of QP levels on choice of basis sets, and differing approximations for dealing with core levels. In all GW calculations presented here, G and W are generated from the local-density approximation (LDA), which we denote as the \GLDA\WLDA approximation. To test its range of validity, the \GLDA\WLDA approximation is applied to a variety of materials systems. We show that for simple sp semiconductors, \GLDA\WLDA always underestimates bandgaps; however, better agreement with experiment is obtained when the self-energy is not renormalized, and we propose a justification for it. Some calculations for Si are compared to pseudopotential-based \GLDA\WLDA calculations, and some aspects of the suitability of pseudopotentials for GW calculations are discussed.Comment: 38 pages,6 figures. Minor Revision

Momentum-resolved spectral functions of SrVO3_3 calculated by LDA+DMFT

LDA+DMFT, the merger of density functional theory in the local density approximation and dynamical mean-field theory, has been mostly employed to calculate k-integrated spectra accessible by photoemission spectroscopy. In this paper, we calculate k-resolved spectral functions by LDA+DMFT. To this end, we employ the Nth order muffin-tin (NMTO) downfolding to set up an effective low-energy Hamiltonian with three t_2g orbitals. This downfolded Hamiltonian is solved by DMFT yielding k-dependent spectra. Our results show renormalized quasiparticle bands over a broad energy range from -0.7 eV to +0.9 eV with small ``kinks'', discernible in the dispersion below the Fermi energy.Comment: 21 pages, 8 figure

Bidirectional PageRank Estimation: From Average-Case to Worst-Case

We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how the reversibility of random walks on undirected networks can be exploited to convert average-case to worst-case guarantees. While past bidirectional methods combine forward random walks with reverse local pushes, our algorithm combines forward local pushes with reverse random walks. We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks.Comment: Workshop on Algorithms and Models for the Web-Graph (WAW) 201