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Simplified decision making in the belief space using belief sparsification [article]

Khen Elimelech, Vadim Indelman
2021 arXiv   pre-print
In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional  ...  state space.  ...  Andrej Kitanov from the Faculty of Aerospace Engineering at the Technion -Israel Institute of Technology, for insightful discussions concerning Section 3.3.2, and his assistance with implementing the simulation  ... 
arXiv:1909.00885v4 fatcat:gur4uqitdng7zimopzenivv5re

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

Bo Liu, Sanfeng Chen, Shuai Li, Yongsheng Liang
2012 Sensors  
In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating  ...  In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis.  ...  than LSPI in high-dimensional space.  ... 
doi:10.3390/s120302632 pmid:22736969 pmcid:PMC3376585 fatcat:hbehozop5rbdhkpw2lkoaisbtq

Kernel Recursive Least Squares Function Approximation in Game Theory Based Control

Hitesh Shah, M. Gopal
2016 Procedia Technology - Elsevier  
Simulation results on two-link robot manipulator show that the proposed method has high learning efficiency better accuracy measured in terms of mean square error; and lesser computation time, compared  ...  A game theoretic aspect in reinforcement learning based controller design with kernel recursive least squares algorithm for value function approximation is proposed in this paper.  ...  experiments on two-link robot manipulator a high dimensional continuous state-space nonlinear system.  ... 
doi:10.1016/j.protcy.2016.03.026 fatcat:ntwyiuihprfm7k435fva74xh34

Guest Editorial Introduction to the Special Issue of the IEEE L-CSS on Learning and Control

Giovanni Cherubini, Martin Guay, Sophie Tarbouriech, Kartik Ariyur, Mireille E. Broucke, Subhrakanti Dey, Christian Ebenbauer, Paolo Frasca, Bahman Gharesifard, Antoine Girard, Joao Manoel Gomes da Silva, Lars Grune (+5 others)
2020 IEEE Control Systems Letters  
of functions that leads to efficient state-discretization in continuous-state Markov decision processes.  ...  Sparsification enables local learning through intelligent state segmentation without relying on a high adaptive learning rate.  ... 
doi:10.1109/lcsys.2020.2986590 fatcat:wx42r4h6ond3dkjntcwsdrmojy

Topological Information-Theoretic Belief Space Planning with Optimality Guarantees [article]

Andrej Kitanov, Vadim Indelman
2019 arXiv   pre-print
Determining a globally optimal solution of belief space planning (BSP) in high-dimensional state spaces is computationally expensive, as it involves belief propagation and objective function evaluation  ...  In this paper we contribute to this body of work a novel method for efficiently determining error bounds of t-bsp, thereby providing global optimality guarantees or uncertainty margin of its solution.  ...  CONCLUSIONS This paper provides theoretical foundations for action consistent topological belief space planning (t-BSP). t-BSP enables efficient decision making in high dimensional state spaces by considering  ... 
arXiv:1903.00927v1 fatcat:22eioiahwng7rn7kib7clc2smy

Least-squares policy iteration algorithms for robotics: Online, continuous, and automatic

Stefan R. Friedrich, Michael Schreibauer, Martin Buss
2019 Engineering applications of artificial intelligence  
Next, borrowing sparsification methods from kernel adaptive filtering, the continuous action-space approximation in the online least-squares policy iteration algorithm can be efficiently automated as well  ...  We then propose a similarity-based information extrapolation for the recursive temporal difference update in order to perform the dictionary expansion step efficiently in both algorithms.  ...  Acknowledgments This work was supported in part within the ERC Advanced Grant SHRINE Agreement No. 267877 and in part by the Technische Universität München-Institute for Advanced Study (  ... 
doi:10.1016/j.engappai.2019.04.001 fatcat:zwyv7u2dmndldblorksygiz6xe

Decoupling Shrinkage and Selection for the Bayesian Quantile Regression [article]

David Kohns, Tibor Szendrei
2021 arXiv   pre-print
The procedure follows two steps: In the first step, we shrink the quantile regression posterior through state of the art continuous priors and in the second step, we sparsify the posterior through an efficient  ...  We apply our two-step approach to a high dimensional growth-at-risk (GaR) exercise.  ...  all model uncertainty, as seen by the large white spaces in figure (6) .  ... 
arXiv:2107.08498v1 fatcat:4mq4qxndqjhujbfuzovfhijeqm

Combinatorial Bayesian Optimization using the Graph Cartesian Product [article]

Changyong Oh and Jakub M. Tomczak and Efstratios Gavves and Max Welling
2019 arXiv   pre-print
Moreover, using the Horseshoe prior for the scale parameter in the ARD diffusion kernel results in an effective variable selection procedure, making COMBO suitable for high dimensional problems.  ...  COMBO outperforms consistently the latest state-of-the-art while maintaining computational and statistical efficiency.  ...  COMBO allows for accurate, efficient and large-scale BO on combinatorial search spaces. In this work, we make the following contributions.  ... 
arXiv:1902.00448v2 fatcat:ogxvc42acja3zgxja62p6xnayu

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration [article]

Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang
2021 arXiv   pre-print
adversarial attack path in the latent space of an autoencoder-based generative model that closely approximates decision boundaries between two or more classes.  ...  Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.  ...  Generative Modeling In order to model the complex high-dimensional space the data lies in, generative models are used to approximate the inaccessible ground truth data distribution.  ... 
arXiv:1912.07458v5 fatcat:dnebtyxpovc3jmlvc5h3esciey

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders [article]

Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone
2021 arXiv   pre-print
For instance, performing motion planning in a high-dimensional latent representation of the environment could be intractable.  ...  Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction  ...  Boris Kirshtein for his advice and assistance.  ... 
arXiv:2010.09164v3 fatcat:pyzjckb4pjalxpr2nrlh4s6vwu

Simultaneous localization and mapping (SLAM): part II

T. Bailey, H. Durrant-Whyte
2006 IEEE robotics & automation magazine  
The great majority of work has focused on improving computational efficiency while ensuring consistent and accurate estimates for the map and vehicle pose.  ...  These include linear-time state augmentation, sparsification in information form, partitioned updates, and submapping methods.  ...  It is a general concept for increasing robustness by accumulating information and permitting delayed decision making.  ... 
doi:10.1109/mra.2006.1678144 fatcat:lkr5hxg2dfhjdlcg7dwgadgl3i

Sparsifying to optimize over multiple information sources: an augmented Gaussian process based algorithm

Antonio Candelieri, Francesco Archetti
2021 Structural And Multidisciplinary Optimization  
This approximation is estimated through a model discrepancy measure and the prediction uncertainty of the GPs.  ...  challenging task known as multi-information source optimization (MISO), where each source has a different cost and the level of approximation (aka fidelity) of each source can change over the search space  ...  Acknowledgements We greatly acknowledge the DEMS Data Science Lab, Department of Economics Management and Statistics (DEMS), University of Milano-Bicocca, for supporting this work by providing computational  ... 
doi:10.1007/s00158-021-02882-7 fatcat:mr2cfcz4h5cgthohk4o6hwojsm

Compressive Information Extraction: A Dynamical Systems Approach1

Mario Sznaier
2012 IFAC Proceedings Volumes  
time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.  ...  THIS PAGE Unclassified REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the  ...  The long term vision was to lay the foundations for designing systems endowed with provably correct flexible autonomy, capable of making decisions in-situ, with minimal human intervention.  ... 
doi:10.3182/20120711-3-be-2027.00430 fatcat:4xxsi2orf5bdlpddghh3fpl65e

A-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems with regularized ℓ_0-sparsification [article]

Alen Alexanderian, Noemi Petra, Georg Stadler, Omar Ghattas
2014 arXiv   pre-print
We present an efficient method for computing A-optimal experimental designs for infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs).  ...  Computing optimal experimental designs (OEDs) is particularly challenging for inverse problems governed by computationally expensive PDE models with infinite-dimensional (or, after discretization, high-dimensional  ...  We would like to thank James Martin for providing us with an implementation of the algorithm in [11] , which was used to compute the application of the inverse square root of the mass matrix.  ... 
arXiv:1308.4084v2 fatcat:pzllmxlnn5aunmy6m3cvqkuhqu

Deep Bayesian Quadrature Policy Optimization [article]

Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue
2020 arXiv   pre-print
In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation.  ...  for several deep policy gradient algorithms, and, (iii) the uncertainty in gradient estimation that can be incorporated to further improve the performance.  ...  Azizzadenesheli is supported in part by Raytheon and Amazon Web Service. A.  ... 
arXiv:2006.15637v3 fatcat:udecffhidrgdlfk36x2zbopyxq
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