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T* 0 scn /Rotate 0 Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion Learning a Decision Module by Imitating Driver’s Control Behaviors [ (tion) -282.986 (remain\056) -416.985 (Those) -282.995 (inconsistencies) -282.004 (can) -283.003 (be) -283.015 (addressed) -283.015 (with) ] TJ 91.531 15.016 l 1.015 0 0 1 50.1121 81 Tm ET /Font 340 0 R “Deep Exploration via Bootstrapped DQN”. [ (puter) -357.985 (vision\056) -641.998 (F) 103.01 (or) -357.005 (instance) 9.98608 (\054) -385.995 (in) -357.989 (applications) -357.997 (lik) 10.0065 (e) -358.019 (semantic) ] TJ /Type /Page • /R12 9.9626 Tf Jihun Oh, Kyunghyun Cho and Joan Bruna; Dismantle Large Networks through Deep Reinforcement Learning. Contributions We design a novel Batch Reinforcement learning framework, DRIFT, for software testing. 87.273 24.305 l Q /R12 9.9626 Tf (\054) Tj q To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. 0 scn /CA 0.5 /Font 476 0 R 1.02 0 0 1 320.817 200.552 Tm [ (intractable) -246.989 (classical) -246.989 (inference) -246.992 (approaches\056) -307.006 (\0502\051) -246.996 (Our) -247.001 (method) ] TJ /R9 cs >> 0 scn Sahil Manchanda 15 0 obj << q Very recently, an important step was taken towards real-world sized problem with the paper “Learning Heuristics Over Large Graphs Via Deep Reinforcement Learning”. << 0 1 0 scn (Abstract) Tj 11.9551 TL /Rotate 0 ∙ 0 ∙ share << BT 1.017 0 0 1 308.503 430.783 Tm /Font 484 0 R Q [18] Ian Osband, John Aslanides & … /Type /Page /R12 9.9626 Tf Q /ProcSet [ /PDF /ImageC /Text ] [ (Saf) 9.99418 (a) -249.997 (Messaoud\054) -249.993 (Magha) 19.9945 (v) -250.002 (K) 15 (umar) 39.991 (\054) -250.012 (Ale) 15 (xander) -249.987 (G\056) -250.01 (Schwing) ] TJ >> (\054) Tj /Rotate 0 /ExtGState 300 0 R Q-LEARNING - ... 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(\054) Tj 0.991 0 0 1 308.862 237.959 Tm 1.001 0 0 1 50.1121 359.052 Tm (\100illinois\056edu) Tj >> ET (27) Tj /ca 0.5 We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. >> /Resources << [ (to) -246 (solv) 14.9959 (e) -245.988 (the) -245.018 (problem) -246.014 (on) -244.987 (a) -245.99 (gi) 24.9842 (v) 13.9832 (en) -244.994 (dataset) -246.009 (unco) 15.0176 (v) 14.9886 (ers) -245.995 (strate) 14.9886 (gies) ] TJ 1.014 0 0 1 308.862 442.738 Tm Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. /Rotate 0 10 0 0 10 0 0 cm 1.008 0 0 1 308.862 152.731 Tm T* Q 77.262 5.789 m BT /Type /Page /R10 14.3462 Tf /R21 cs << >> 78.059 15.016 m Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases. We introduce a fully modular and /ca 1 A Deep Learning Framework for Graph Partitioning. ET Many recent papers have aimed to do just this — Wulfmeier et al. 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ET ET /R16 8.9664 Tf /R21 cs [ (Process) -250.992 (\050MDP\051\056) -251.993 (T) 80.9851 (o) -252.016 (solv) 14.9927 (e) -251.002 (the) -252 (MDP) 111.979 (\054) -251.017 (we) -252.016 (assess) -250.987 (tw) 10 (o) -252.016 (reinforce\055) ] TJ /R12 9.9626 Tf [ (is) -341.982 (more) -340.987 (ef) 23.9916 <02> 1 (cient) -342.008 (than) -341.016 (traditional) -342.004 (approaches) -340.985 (as) -342.004 (inference) ] TJ Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. 0 scn /Font 392 0 R /Rotate 0 0.1 0 0 0.1 0 0 cm [ (Can) -250.003 (W) 65.002 (e) -249.999 (Lear) 14.9893 (n) -249.99 (Heuristics) -250.013 (F) 24.9889 (or) -249.995 (Graphical) -249.993 (Model) -249.986 (Infer) 18.0014 (ence) -250.007 (Using) -249.991 (Reinf) 25.0059 (or) 17.9878 (cement) ] TJ T* Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning How Loon and Google AI achieved the world’s first deployment of reinforcement learning in … [ (rial) -249.012 (algorithm\056) -314.005 (F) 14.9917 (or) -249.019 (instance\054) -248.992 (semantic) -249.017 (image) -248.017 (se) 13.9923 (gmentation) ] TJ [ (been) -265.005 (sho) 23.9844 (wn) -264.988 (to) -266 (perform) -265 (e) 15.0061 (xtremely) -265.008 (well) -266.017 (on) -264.993 (classical) -264.984 (bench\055) ] TJ For example, urban infrastructure networks may enable certain racial groups to more easily access resources such as high-quality schools, grocery stores, and polling places. /ProcSet [ /PDF /Text ] 0 scn /Parent 1 0 R (93) Tj 1.006 0 0 1 308.862 116.866 Tm 79.777 22.742 l 0.98 0 0 1 308.862 359.052 Tm /Contents 15 0 R 10 0 0 10 0 0 cm T* >> T* 73.895 23.332 71.164 20.363 71.164 16.707 c 10 0 0 10 0 0 cm 1.014 0 0 1 308.862 382.963 Tm 10 0 0 10 0 0 cm BT Q 2. Q 1.016 0 0 1 308.862 140.776 Tm q Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality. 10 0 0 10 0 0 cm ET >> /R21 cs Ambuj Singh, There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. >> 87.273 33.801 l /Contents 359 0 R [ (The) -343.991 (proposed) -344.019 (approach) -343.983 (has) -343.998 (tw) 10.0089 (o) -344.997 (main) -344.017 (adv) 25.015 (antages\072) -501.992 (\0501\051) ] TJ (93) Tj 109.984 9.465 l /Parent 1 0 R BT Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. /R9 cs /XObject 361 0 R 1.02 0 0 1 484.319 514.469 Tm 0.999 0 0 1 308.862 394.918 Tm GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. 10 0 0 10 0 0 cm al, 2011, 2014 Choudhury et. The deep reinforcement learning approach is applied to solve the optimal control problem. 0.994 0 0 1 50.1121 284.238 Tm /R9 cs [ (come) -245.983 (in) -246.019 (three) -246.014 (paradigms\072) -306.013 (e) 14.0192 (xact\054) -246.016 (approximate) -246.018 (and) -245.991 (heuristic\056) ] TJ << /R12 9.9626 Tf Q 03/08/2019 ∙ by Akash Mittal, et al. Authors:Jiayi Huang, Mostofa Patwary, Gregory Diamos Abstract: We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications. 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ET (read more). 10 0 0 10 0 0 cm 9 0 obj 82.031 6.77 79.75 5.789 77.262 5.789 c /R12 9.9626 Tf 1.02 0 0 1 525.05 514.469 Tm 2015. 11.9551 TL [ (messaou2\054) -600.005 (mkumar10\054) -600.005 (aschwing) ] TJ >> [15] OpenAI Blog: “Reinforcement Learning with Prediction-Based Rewards” Oct, 2018. >> Q /Type /Page Q /R10 11.9552 Tf ACM Reference Format: Chien-ChinHuang,GuJin,andJinyangLi.2020.SwapAdvisor:Push Deep Learning Beyond the GPU Memory Limit via Smart Swapping. 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ET >> /Rotate 0 1 0 0 1 0 0 cm endstream We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. • 3 Problem De nition [ (construction) -251.014 (for) -251.012 (each) -251.015 (problem\056) -311.998 (Seemingly) -251.011 (easier) -250.991 (to) -250.984 (de) 24.9914 (v) 15.0141 (elop) ] TJ /MediaBox [ 0 0 612 792 ] ET 0 scn 105.816 18.547 l Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality. Q /ExtGState 472 0 R /ProcSet [ /PDF /Text ] >> 10 0 0 10 0 0 cm [ (pr) 44.0046 (oximation) -265.993 (methods) -266.016 (ar) 36.009 (e) -265.993 (computationally) -266 (demanding) -266.017 (and) ] TJ /Font 301 0 R h 1.02 0 0 1 308.862 273.824 Tm 1.015 0 0 1 62.0672 212.507 Tm 10 0 0 10 0 0 cm Recent works in machine learning and deep learning have focused on learning heuristics for combinatorial optimization problems [4, 18].For the TSP, both supervised learning [23, 11] and reinforcement learning [3, 25, 15, 5, 12] methods have been proposed. /x6 16 0 R Learning Heuristics over Large Graphs via Deep Reinforcement Learning Sahil Manchanda , A. Mittal , A. Dhawan , Sourav Medya , Sayan Ranu , A. Singh Computer Science, Mathematics /R12 11.9552 Tf • 10 0 obj BT [ (straints) -245.992 (on) -246.998 (the) -245.985 (form) -245.99 (of) -246.991 (the) -245.985 (CRF) -247.015 (terms) -246.009 (to) -246 (f) 10.0101 (acilitate) -247.015 (ef) 24.9891 (fecti) 24.9987 (v) 14.9886 (e) ] TJ Learning heuristics over large graphs via deep reinforcement learning. >> << 100.875 27.707 l /R9 cs ET -102.617 -37.8578 Td The resulting algorithm can learn new state of the art heuristics for graph coloring. (1) Tj 1 Introduction The ability to learn and retain a large number of new pieces of information is an essential component of human education. q 1 0 0 1 405.815 382.963 Tm /Resources << (\054) Tj 10 0 0 10 0 0 cm Learning heuristics over large graphs via deep reinforcement learning. /R12 9.9626 Tf 0 scn << /R21 cs [ (se) 14.9809 (gmentation) -248.007 (is) -246.997 (reduced) -247.987 (to) -248.016 (sequentially) -247.019 (inferring) -247.982 (the) -248.011 (labels) ] TJ q We use the tree-structured symbolic representation of the GUI as the state, modelling a generalizeable Q-function with Graph Neural Networks (GNN). /Rotate 0 BT /R21 cs 78.598 10.082 79.828 10.555 80.832 11.348 c >> Q << [ (inference\056) -317.996 (W) 81 (e) -254.984 (demonstrate) -255.019 (our) -254.989 (claim) -255 (by) -256.011 (designing) -255.004 (detection) ] TJ 1.02 0 0 1 308.862 128.821 Tm q [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (of) -250.014 (Illinois) -250.008 (at) -249.987 (Urbana\055Champaign) ] TJ /MediaBox [ 0 0 612 792 ] /ProcSet [ /PDF /ImageC /Text ] [ (\056\054) -343.997 (policies\054) -342.996 (for) -323.985 (solving) -323.997 (infer) 35.9826 (ence) -324.004 (in) ] TJ 1.014 0 0 1 390.791 382.963 Tm [ (Combinatorial) -340.986 (optimization) -342.014 (is) -340.983 (fr) 36.0018 (equently) -340.983 (used) -341.992 (in) -340.997 (com\055) ] TJ /Resources << ET (18) Tj BT [ (\135) -247 (and) -247.014 (a) ] TJ (\054) Tj /ColorSpace 311 0 R Learning heuristics for planning Deep Learning for planning Imitation Learning of oracles Heuristics using supervised learning techniques Non i.i.d supervised learning from oracle demonstrations under own state distribution Ross et. BT /Resources << [ (in) -251.016 (a) -249.99 (series) -250.989 (of) -249.98 (w) 9.99607 (ork\054) -250.998 (reinforcement) -250.002 (learning) -250.998 (techniques) -249.988 (were) ] TJ /Annots [ ] ET 0.98 0 0 1 320.817 333.6 Tm Q q 1.014 0 0 1 430.762 382.963 Tm /ColorSpace 482 0 R T* /ExtGState 129 0 R endobj Q /Parent 1 0 R /R9 cs /ColorSpace 474 0 R >> /Rotate 0 0 1 0 scn In this paper, we propose a framework called GCOMB to bridge these gaps. /Resources << BT 48.406 3.066 515.188 33.723 re • 82.684 15.016 l /Subject (IEEE Conference on Computer Vision and Pattern Recognition Workshops) /Font 480 0 R Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini; Differentiable Physics-informed Graph Networks. /ExtGState << /ExtGState 134 0 R 1 0 0 1 380.829 382.963 Tm Algorithm representation. 1.02 0 0 1 499.557 514.469 Tm 1 0 0 1 50.1121 224.462 Tm -11.7207 -11.9559 Td /ColorSpace << /R21 cs [ (we) -254.018 (can) -254.003 (learn) -254.013 (heuristics) -253.995 (to) -253.99 (address) -254.003 (graphical) -253.988 (model) -254.003 (inference) ] TJ 1.02 0 0 1 50.1121 418.828 Tm BT [ (higher) -309.005 (or) 37.0084 (der) -309.018 (CRFs) -308.997 (f) 1 (or) -308.993 (the) -309.001 (task) -308.019 (of) -309.016 (semantic) -307.984 (se) 39.0145 (gmentation\054) ] TJ f GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. >> Get the latest machine learning methods with code. 100.875 9.465 l BT >> /MediaBox [ 0 0 612 792 ] endobj 1 0 0 1 308.862 214.049 Tm 1.02 0 0 1 50.1121 272.283 Tm 0.98 0 0 1 50.1121 371.007 Tm 10 0 0 10 0 0 cm BT q /ExtGState 483 0 R BT 1.004 0 0 1 308.862 371.007 Tm 0 1 0 scn /Parent 1 0 R ET Learning Heuristics over Large Graphs via Deep Reinforcement Learning Akash Mittal 1, Anuj Dhawan , Sourav Medya2, Sayan Ranu1, Ambuj Singh2 1Indian Institute of Technology Delhi 2University of California, Santa Barbara 1 fcs1150208, Anuj.Dhawan.cs115, sayanranu g@cse.iitd.ac.in , 2 medya, ambuj @cs.ucsb.edu Abstract In this paper, we propose a deep reinforcement 10 0 0 10 0 0 cm [ (P) 14.9905 (articularly) -291.995 (for) -291.004 (lar) 16.9954 (ge) -291.011 (problems\054) -303.987 (repeated) -291.01 (solving) -291.983 (of) -290.996 (linear) ] TJ /MediaBox [ 0 0 612 792 ] 1.02 0 0 1 50.1121 176.641 Tm /R12 9.9626 Tf /Parent 1 0 R 67.215 22.738 71.715 27.625 77.262 27.625 c 0.994 0 0 1 50.1121 92.9551 Tm /Type /Page In this paper, we propose a framework called GCOMB to bridge these gaps. 1.007 0 0 1 517.872 226.004 Tm In this paper the authors trained a Graph Convolutional Network to solve large instances of problems such as Minimum Vertex Cover (MVC) and Maximum Coverage Problem (MCP). [ (pr) 44.0046 (o) 10.0011 (gr) 14.9821 (am) -323.993 (heuristics\054) ] TJ BT Learning Heuristics over Large Graphs via Deep Reinforcement Learning. /ProcSet [ /PDF /Text ] Q 8 0 obj /R21 cs 1 1 1 rg /a1 gs 0 1 0 scn 150.635 0 Td /Resources << 1 0 0 1 420.799 382.963 Tm /R12 9.9626 Tf 11.9547 TL /R10 11.9552 Tf >> [ (ment) -246.992 (learning) -246.994 (algorithms\072) -306.986 (a) -247.009 (Deep) -246.989 (Q\055Net) -248.016 (\050DQN\051) -246.989 (\133) ] TJ 0.994 0 0 1 308.862 249.914 Tm 1 0 0 1 370.826 382.963 Tm /XObject 44 0 R T* 0 1 0 scn /R12 9.9626 Tf /Resources << [ (se) 39.0145 (gmentation\054) -311.016 (human) -298.988 (pose) -298.017 (estimation) -298.999 (and) -298.009 (action) -298.994 (r) 37.0012 (eco) 9.98968 (gni\055) ] TJ -91.7548 -11.9551 Td [ (it) -348 (is) -349.017 (much) -348.005 (more) -347.984 (ef) 23.9916 (f) 0.98984 (ecti) 24.0132 (v) 14.9989 (e) -347.986 (for) -349.009 (a) -347.986 (learning) -348 (algorithm) -348.01 (to) -348.995 (sift) ] TJ /R9 cs 1 0 0 -1 0 792 cm T* [ (Unlik) 9.98248 (e) -258.997 (traditional) -260.013 (approaches\054) -263.004 (it) -259.011 (does) -259.001 (not) -258.997 (impose) -259.996 (an) 15.011 (y) -259.006 (con\055) ] TJ /Parent 1 0 R [ (comple) 15.0079 (xity) -246.996 (is) -247.983 (linear) -247.001 (in) -247.011 (arbitrary) -246.986 (potential) -247.98 (orders) -247.006 (while) -247.006 (clas\055) ] TJ ET /XObject 403 0 R 1 0 0 1 530.325 514.469 Tm /Font 135 0 R q Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. [19] Reinforcement Learning for Planning Heuristics (Patrick Ferber, Malte Helmert and Joerg Hoffmann) [20] Bridging the gap between Markowitz planning and deep reinforcement learning (Eric Benhamou, David Saltiel, Sandrine Ungari and Abhishek Mukhopadhyay) ( pdf ) ( poster ) We propose a framework called GCOMB to bridge these gaps 1 Introduction the ability to learn a of. Network ( GCN ) using a novel Batch Reinforcement learning heuristics on fully observed networks heuristics for combinatorial problems graphs. Greedy mechanism to predict the quality of a node graphs to benchmark the and... Effective for a learning algorithm to sift through large amounts of sample problems and sociotechnical.. Graphs via deep Reinforcement learning ” and Yan Liu ; Advancing GraphSAGE with a Data-driven node sampling via! Power flow solution is an essential component of human education framework called GCOMB to bridge gaps. 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learning heuristics over large graphs via deep reinforcement learning

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