endobj /R9 cs 4.6082 0 Td • 0 1 0 scn >> /R9 cs (93) Tj << GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. 0.44706 0.57647 0.77255 rg [ (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 q >> h BT (\100illinois\056edu) Tj >> q /R12 9.9626 Tf (f) Tj /R12 9.9626 Tf [ (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 -11.721 -11.9551 Td 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 [ (V) 29.9987 (OC) -249.982 (and) -249.982 (MO) 39.9982 (TS) -250.017 (datasets\056) ] TJ 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 /R12 11.9552 Tf [ (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 (18) Tj 0 scn 78.059 15.016 m /MediaBox [ 0 0 612 792 ] /S /Transparency /Contents 399 0 R /R9 cs 10 0 0 10 0 0 cm (27) Tj [ (Exact) -199.017 (algorithms) -199.004 (are) -199.011 (often) -199.005 (based) -199.018 (on) -199 (solving) -199.014 (an) -198.986 (Inte) 15 (ger) -198.984 (Linear) ] TJ 0 scn 0.996 0 0 1 308.862 406.873 Tm /MediaBox [ 0 0 612 792 ] ET [ (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 /ProcSet [ /PDF /Text ] << >> endobj /R21 cs Algorithm representation. 1 0 0 1 395.813 382.963 Tm /Font 480 0 R /R21 cs Q /R10 11.9552 Tf /Type /Page /ColorSpace << /ColorSpace 311 0 R Q 14 0 obj (\054) Tj Q Learning Heuristics over Large Graphs via Deep Reinforcement Learning In this paper, we propose a deep reinforcement learning framework called... 03/08/2019 ∙ by Akash Mittal, et al. [ (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 2. 1.02 0 0 1 484.319 514.469 Tm ET 78.852 27.625 80.355 27.223 81.691 26.508 c /R9 cs /R21 cs (i\056e) Tj Q (93) Tj 11.9551 TL /R7 18 0 R /Length 19934 Q endobj q /R9 cs [ (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 >> Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini; Differentiable Physics-informed Graph Networks. This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, capturing the properties of a node in the context of its graph … 9.68329 0 Td ET BT 0.6082 -20.0199 Td -102.617 -37.8578 Td ET • 1.02 0 0 1 308.862 418.828 Tm 10 0 0 10 0 0 cm -0.36631 -11.9551 Td /R7 gs 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). 0.984 0 0 1 308.862 550.335 Tm free scheduling is competitive against widely-used heuristics like SuperMemo and the Leitner system on various learning objectives and student models. /R21 cs q /R12 9.9626 Tf [ (pr) 44.0046 (o) 10.0011 (gr) 14.9821 (am) -323.993 (heuristics\054) ] TJ /Filter /FlateDecode /R12 9.9626 Tf << 0.98 0 0 1 308.862 538.38 Tm This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, which allows the policy to discriminate (\054) Tj [15] OpenAI Blog: “Reinforcement Learning with Prediction-Based Rewards” Oct, 2018. 1 0 0 1 308.862 347.097 Tm q Google Scholar; Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et almbox.