Reinforcement Learning on Graphs: A Survey

Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks. However, these graph mining methods and RL models are dispersed in different research areas, which makes it hard to compare them. In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method descriptions, open-source codes, and benchmark datasets of GRL methods. Furthermore, we propose important directions and challenges to be solved in the future. As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars. In addition, we create an online open-source for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.

[1]  Senzhang Wang,et al.  Multi-View Tensor Graph Neural Networks Through Reinforced Aggregation , 2023, IEEE Transactions on Knowledge and Data Engineering.

[2]  Chunping Wang,et al.  NetRL: Task-Aware Network Denoising via Deep Reinforcement Learning , 2023, IEEE Transactions on Knowledge and Data Engineering.

[3]  Hao Peng,et al.  Reinforced, Incremental and Cross-Lingual Event Detection From Social Messages , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Changyin Sun,et al.  Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Shuiwang Ji,et al.  Explainability in Graph Neural Networks: A Taxonomic Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Philip S. Yu,et al.  Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market , 2020, IEEE Transactions on Knowledge and Data Engineering.

[7]  Mengqi Hu,et al.  Deep Reinforcement Learning With Graph Representation for Vehicle Repositioning , 2022, IEEE Transactions on Intelligent Transportation Systems.

[8]  Weizi Li,et al.  SparRL: Graph Sparsification via Deep Reinforcement Learning , 2022, SIGMOD Conference.

[9]  D. Bacciu,et al.  Explaining Deep Graph Networks via Input Perturbation , 2022, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Jessica J. M. Monaghan,et al.  Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis , 2022, Neural Networks.

[11]  Shuiwang Ji,et al.  Automated Data Augmentations for Graph Classification , 2022, ICLR.

[12]  Dongqi Wang,et al.  Network Representation Learning Algorithm Based on Complete Subgraph Folding , 2022, Mathematics.

[13]  Dongqi Wang,et al.  Network Embedding Algorithm Taking in Variational Graph AutoEncoder , 2022, Mathematics.

[14]  Xiaoyu Yang,et al.  Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection , 2022, 2023 International Joint Conference on Neural Networks (IJCNN).

[15]  Tongrang Fan,et al.  A novel embedding learning framework for relation completion and recommendation based on graph neural network and multi-task learning , 2022, Soft Computing - A Fusion of Foundations, Methodologies and Applications.

[16]  Egemen Tanin,et al.  Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning , 2022, ArXiv.

[17]  Wen Zheng,et al.  Learning self-driven collective dynamics with graph networks , 2022, Scientific reports.

[18]  Hao Liu,et al.  Dynamic knowledge graph reasoning based on deep reinforcement learning , 2022, Knowl. Based Syst..

[19]  Chengqing Yu,et al.  A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network , 2022, Digit. Signal Process..

[20]  Yuehua Wu,et al.  AFGSL: Automatic Feature Generation based on Graph Structure Learning , 2021, Knowl. Based Syst..

[21]  Yansheng Li,et al.  RLPath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning , 2021, Applied Intelligence.

[22]  Qiang He,et al.  Hypernetwork Dismantling via Deep Reinforcement Learning , 2021, IEEE Transactions on Network Science and Engineering.

[23]  Philip S. Yu,et al.  Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks , 2021, ACM Trans. Inf. Syst..

[24]  Denis Larocque,et al.  IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control , 2020, IEEE Transactions on Intelligent Transportation Systems.

[25]  Linlin Ou,et al.  Graph pruning for model compression , 2019, Applied Intelligence.

[26]  A. Cabellos-Aparicio,et al.  Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case , 2019, Comput. Commun..

[27]  Xiao Huang,et al.  Auto-GNN: Neural architecture search of graph neural networks , 2019, Frontiers in Big Data.

[28]  Enhong Chen,et al.  STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control , 2019, IEEE Transactions on Mobile Computing.

[29]  Philip S. Yu,et al.  Adversarial Attack and Defense on Graph Data: A Survey , 2018 .

[30]  Joseph J. Lim,et al.  Know Your Action Set: Learning Action Relations for Reinforcement Learning , 2022, International Conference on Learning Representations.

[31]  Furong Huang,et al.  Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory , 2022, ICLR.

[32]  Kee-Eung Kim,et al.  Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning , 2022, International Conference on Learning Representations.

[33]  Longbiao Chen,et al.  RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems , 2023, IEEE Transactions on Mobile Computing.

[34]  Victor C. M. Leung,et al.  Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning , 2023, IEEE Transactions on Emerging Topics in Computational Intelligence.

[35]  Peng Wang,et al.  Multi-hop Knowledge Graph Reasoning Based on Hyperbolic Knowledge Graph Embedding and Reinforcement Learning , 2021, IJCKG.

[36]  Wenwu Zhu,et al.  GQNAS: Graph Q Network for Neural Architecture Search , 2021, Industrial Conference on Data Mining.

[37]  Haizhou Du,et al.  Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning , 2021, ArXiv.

[38]  Bowen Du,et al.  Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning , 2021, Inf. Sci..

[39]  Senzhang Wang,et al.  ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network , 2021, 2021 IEEE International Conference on Data Mining (ICDM).

[40]  Zhen Han,et al.  TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting , 2021, EMNLP.

[41]  Maosong Sun,et al.  Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks , 2021, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Samuel Labi,et al.  Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles , 2021, Comput. Aided Civ. Infrastructure Eng..

[43]  Bolin Ding,et al.  Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning , 2021, SIGIR.

[44]  Zhitang Chen,et al.  Ordering-Based Causal Discovery with Reinforcement Learning , 2021, IJCAI.

[45]  E. Paquet,et al.  Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management - DeepPocket , 2021, Expert Syst. Appl..

[46]  Zheng Zeng,et al.  GraphLight: Graph-based Reinforcement Learning for Traffic Signal Control , 2021, 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS).

[47]  A. S. M. Ahsan-Ul-Haque,et al.  Reinforcement Learning For Data Poisoning on Graph Neural Networks , 2021, SBP-BRiMS.

[48]  Shuiwang Ji,et al.  On Explainability of Graph Neural Networks via Subgraph Explorations , 2021, ICML.

[49]  Hao Peng,et al.  SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism , 2021, WWW.

[50]  Huifang Ma,et al.  Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction , 2021, 2020 25th International Conference on Pattern Recognition (ICPR).

[51]  Mingzhi Mao,et al.  MemoryPath: A deep reinforcement learning framework for incorporating memory component into knowledge graph reasoning , 2021, Neurocomputing.

[52]  Zehua Guo,et al.  Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement , 2021, IEEE Communications Letters.

[53]  Govardana Sachithanandam Ramachandran,et al.  GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning , 2020, AIES.

[54]  Hari Mohan Pandey,et al.  DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning , 2020, Neural Networks.

[55]  Sixing Yu,et al.  Auto Graph Encoder-Decoder for Neural Network Pruning , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[56]  Tao Ren,et al.  A survey of community detection methods in multilayer networks , 2020, Data Mining and Knowledge Discovery.

[57]  Shie Mannor,et al.  Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks , 2020, ICML.

[58]  Shouling Ji,et al.  Graph Backdoor , 2020, USENIX Security Symposium.

[59]  Jinyuan Jia,et al.  Backdoor Attacks to Graph Neural Networks , 2020, SACMAT.

[60]  Louis-Martin Rousseau,et al.  Learning TSP Requires Rethinking Generalization , 2020, ArXiv.

[61]  Yanquan Zhou,et al.  Hierarchical Policy Network with Multi-agent for Knowledge Graph Reasoning Based on Reinforcement Learning , 2021, KSEM.

[62]  Yiming Liu,et al.  A Reinforcement Learning Model for Influence Maximization in Social Networks , 2021, DASFAA.

[63]  Ali Jannesari,et al.  GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning , 2021, ArXiv.

[64]  Jie Cao,et al.  GRL: Knowledge graph completion with GAN-based reinforcement learning , 2020, Knowl. Based Syst..

[65]  Bo Zong,et al.  Parameterized Explainer for Graph Neural Network , 2020, NeurIPS.

[66]  Cheng-te Li,et al.  Reinforcement Learning Enhanced Heterogeneous Graph Neural Network , 2020, ArXiv.

[67]  My T. Thai,et al.  PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks , 2020, NeurIPS.

[68]  Philip S. Yu,et al.  Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters , 2020, CIKM.

[69]  Yu Lei,et al.  Reinforcement Learning based Recommendation with Graph Convolutional Q-network , 2020, SIGIR.

[70]  Kunpeng Liu,et al.  Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams , 2020, KDD.

[71]  Gholamreza Haffari,et al.  Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning , 2020, IJCAI.

[72]  Edwin R. Hancock,et al.  Learning for Graph Matching and Related Combinatorial Optimization Problems , 2020, IJCAI.

[73]  Alexander J. Smola,et al.  An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph , 2020, KDD.

[74]  Xia Hu,et al.  Policy-GNN: Aggregation Optimization for Graph Neural Networks , 2020, KDD.

[75]  Weinan Zhang,et al.  Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning , 2020, SIGIR.

[76]  Shuiwang Ji,et al.  XGNN: Towards Model-Level Explanations of Graph Neural Networks , 2020, KDD.

[77]  D. Kell,et al.  DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach , 2020, Journal of Cheminformatics.

[78]  Marc-Alexandre Côté,et al.  Graph Policy Network for Transferable Active Learning on Graphs , 2020, NeurIPS.

[79]  Qi Wang,et al.  ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning , 2020, Knowl. Based Syst..

[80]  Fengguang Song,et al.  OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems , 2020, ICCS.

[81]  Yizhou Sun,et al.  Finding key players in complex networks through deep reinforcement learning , 2020, Nature Machine Intelligence.

[82]  Chengsong Wang,et al.  Learning Network Representation Through Reinforcement Learning , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[83]  Song Han,et al.  GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning , 2020, 2020 57th ACM/IEEE Design Automation Conference (DAC).

[84]  Shuiwang Ji,et al.  StructPool: Structured Graph Pooling via Conditional Random Fields , 2020, ICLR.

[85]  Suhang Wang,et al.  Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach , 2020, WWW.

[86]  Dechang Pi,et al.  Network representation learning: a systematic literature review , 2020, Neural Computing and Applications.

[87]  Tong Li,et al.  Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks , 2020, IEEE Journal on Selected Areas in Communications.

[88]  Seshadhri Comandur,et al.  The impossibility of low-rank representations for triangle-rich complex networks , 2020, Proceedings of the National Academy of Sciences.

[89]  Regina Barzilay,et al.  Multi-Objective Molecule Generation using Interpretable Substructures , 2020, ICML.

[90]  Matthew J. Hausknecht,et al.  Graph Constrained Reinforcement Learning for Natural Language Action Spaces , 2020, ICLR.

[91]  Demis Hassabis,et al.  Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.

[92]  Mohammed J. Zaki,et al.  Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation , 2019, ICLR.

[93]  Zhitang Chen,et al.  Causal Discovery with Reinforcement Learning , 2019, ICLR.

[94]  Tiejun Huang,et al.  Graph Convolutional Reinforcement Learning , 2018, ICLR.

[95]  Chengqi Zhang,et al.  Network Representation Learning: A Survey , 2017, IEEE Transactions on Big Data.

[96]  Samuel Henrique Silva,et al.  Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization , 2020, IEEE Access.

[97]  Xin Wang,et al.  Coordinated Learning for Lane Changing Based on Coordination Graph and Reinforcement Learning , 2020 .

[98]  Junxing Zhang,et al.  DKDR: An Approach of Knowledge Graph and Deep Reinforcement Learning for Disease Diagnosis , 2019, 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom).

[99]  Qiang Ma,et al.  Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning , 2019, ArXiv.

[100]  M. de Rijke,et al.  Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning , 2019, CIKM.

[101]  Rong Pan,et al.  Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning , 2019, EMNLP.

[102]  Abdelkader Outtagarts,et al.  A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding , 2019, IEEE Transactions on Network and Service Management.

[103]  Hongyuan Zha,et al.  Learning Robust Representations with Graph Denoising Policy Network , 2019, 2019 IEEE International Conference on Data Mining (ICDM).

[104]  Rishabh Singh,et al.  Learning Transferable Graph Exploration , 2019, NeurIPS.

[105]  Albert Cabellos-Aparicio,et al.  Challenging the generalization capabilities of Graph Neural Networks for network modeling , 2019, SIGCOMM Posters and Demos.

[106]  Bo An,et al.  Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks , 2019, IJCAI.

[107]  Ming Zhang,et al.  Ekar: An Explainable Method for Knowledge Aware Recommendation , 2019, 1906.09506.

[108]  Yongfeng Zhang,et al.  Reinforcement Knowledge Graph Reasoning for Explainable Recommendation , 2019, SIGIR.

[109]  Suhang Wang,et al.  Attacking Graph Convolutional Networks via Rewiring , 2019, ArXiv.

[110]  Masahiro Morikura,et al.  Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[111]  Yang Gao,et al.  GraphNAS: Graph Neural Architecture Search with Reinforcement Learning , 2019, ArXiv.

[112]  Weifeng Lv,et al.  Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[113]  Yanfang Ye,et al.  Heterogeneous Graph Attention Network , 2019, WWW.

[114]  J. Leskovec,et al.  GNNExplainer: Generating Explanations for Graph Neural Networks , 2019, NeurIPS.

[115]  Albert Cabellos-Aparicio,et al.  Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN , 2019, SOSR.

[116]  Laura Toni,et al.  Representation Learning on Graphs: A Reinforcement Learning Application , 2019, AISTATS.

[117]  Svetha Venkatesh,et al.  Graph Transformation Policy Network for Chemical Reaction Prediction , 2018, KDD.

[118]  Mark O. Riedl,et al.  Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning , 2018, NAACL.

[119]  Pietro Liò,et al.  Deep Graph Infomax , 2018, ICLR.

[120]  Sergey Levine,et al.  Diversity is All You Need: Learning Skills without a Reward Function , 2018, ICLR.

[121]  Yoshihide Sekimoto,et al.  Deep Reinforcement Learning Approach for Train Rescheduling Utilizing Graph Theory , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[122]  Yuanzhuo Wang,et al.  Path Reasoning over Knowledge Graph: A Multi-agent and Reinforcement Learning Based Method , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[123]  Takayoshi Yoshimura,et al.  Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[124]  Jiawei Han,et al.  Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[125]  Mingjie Sun,et al.  Data Poisoning Attack against Unsupervised Node Embedding Methods , 2018, ArXiv.

[126]  Richard Socher,et al.  Multi-Hop Knowledge Graph Reasoning with Reward Shaping , 2018, EMNLP.

[127]  Huaglory Tianfield,et al.  A Reinforcement Learning Approach for Attack Graph Analysis , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[128]  Lu Chen,et al.  Structured Dialogue Policy with Graph Neural Networks , 2018, COLING.

[129]  Zhengyang Wang,et al.  Large-Scale Learnable Graph Convolutional Networks , 2018, KDD.

[130]  Ryan A. Rossi,et al.  Graph Classification using Structural Attention , 2018, KDD.

[131]  Praveen Chandar,et al.  An Information Retrieval Framework for Contextual Suggestion Based on Heterogeneous Information Network Embeddings , 2018, SIGIR.

[132]  Jure Leskovec,et al.  Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.

[133]  Le Song,et al.  Adversarial Attack on Graph Structured Data , 2018, ICML.

[134]  Nicola De Cao,et al.  MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.

[135]  Sergey Levine,et al.  Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.

[136]  Stephan Günnemann,et al.  Adversarial Attacks on Neural Networks for Graph Data , 2018, KDD.

[137]  Tamer Basar,et al.  Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents , 2018, ICML.

[138]  Liang Zhang,et al.  Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning , 2018, KDD.

[139]  Sanja Fidler,et al.  NerveNet: Learning Structured Policy with Graph Neural Networks , 2018, ICLR.

[140]  Cao Xiao,et al.  FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling , 2018, ICLR.

[141]  Alexander J. Smola,et al.  Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning , 2017, ICLR.

[142]  William Yang Wang,et al.  KBGAN: Adversarial Learning for Knowledge Graph Embeddings , 2017, NAACL.

[143]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[144]  Lexing Xie,et al.  Action Schema Networks: Generalised Policies with Deep Learning , 2017, AAAI.

[145]  Pasquale Minervini,et al.  Convolutional 2D Knowledge Graph Embeddings , 2017, AAAI.

[146]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[147]  Pratik Rane,et al.  Self-Critical Sequence Training for Image Captioning , 2018 .

[148]  Wenlong Fu,et al.  Model-based reinforcement learning: A survey , 2018 .

[149]  M. Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[150]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[151]  Wenhan Xiong,et al.  DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning , 2017, EMNLP.

[152]  Alec Radford,et al.  Proximal Policy Optimization Algorithms , 2017, ArXiv.

[153]  Romain Laroche,et al.  Hybrid Reward Architecture for Reinforcement Learning , 2017, NIPS.

[154]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[155]  Le Song,et al.  2 Common Formulation for Greedy Algorithms on Graphs , 2018 .

[156]  Peng Peng,et al.  Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games , 2017, 1703.10069.

[157]  Tom Schaul,et al.  FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.

[158]  Vijay S. Pande,et al.  MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.

[159]  Youyong Kong,et al.  Deep Direct Reinforcement Learning for Financial Signal Representation and Trading , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[160]  Zeb Kurth-Nelson,et al.  Learning to reinforcement learn , 2016, CogSci.

[161]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[162]  Shie Mannor,et al.  A Deep Hierarchical Approach to Lifelong Learning in Minecraft , 2016, AAAI.

[163]  Jan Hendrik Witte,et al.  Deep Learning for Finance: Deep Portfolios , 2016 .

[164]  Guillaume Bouchard,et al.  Complex Embeddings for Simple Link Prediction , 2016, ICML.

[165]  Rob Fergus,et al.  Learning Multiagent Communication with Backpropagation , 2016, NIPS.

[166]  Shimon Whiteson,et al.  Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.

[167]  Pieter Abbeel,et al.  Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.

[168]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[169]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[170]  Tom Schaul,et al.  Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.

[171]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[172]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[173]  Peter Stone,et al.  Deep Recurrent Q-Learning for Partially Observable MDPs , 2015, AAAI Fall Symposia.

[174]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[175]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[176]  Michael Gamon,et al.  Representing Text for Joint Embedding of Text and Knowledge Bases , 2015, EMNLP.

[177]  Fabian M. Suchanek,et al.  YAGO3: A Knowledge Base from Multilingual Wikipedias , 2015, CIDR.

[178]  Guy Lever,et al.  Deterministic Policy Gradient Algorithms , 2014, ICML.

[179]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[180]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[181]  Tom M. Mitchell,et al.  Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues , 2013, EMNLP.

[182]  Arjun Mukherjee,et al.  What Yelp Fake Review Filter Might Be Doing? , 2013, ICWSM.

[183]  Hiroshi Kawano,et al.  Hierarchical sub-task decomposition for reinforcement learning of multi-robot delivery mission , 2013, 2013 IEEE International Conference on Robotics and Automation.

[184]  Jure Leskovec,et al.  From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.

[185]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[186]  John C. Platt,et al.  Learning Discriminative Projections for Text Similarity Measures , 2011, CoNLL.

[187]  Kurt Mehlhorn,et al.  Weisfeiler-Lehman Graph Kernels , 2011, J. Mach. Learn. Res..

[188]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[189]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[190]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[191]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[192]  J. Leskovec,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[193]  George Karypis,et al.  Comparison of descriptor spaces for chemical compound retrieval and classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[194]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[195]  Hans-Peter Kriegel,et al.  Protein function prediction via graph kernels , 2005, ISMB.

[196]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[197]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[198]  P. Dobson,et al.  Distinguishing enzyme structures from non-enzymes without alignments. , 2003, Journal of molecular biology.

[199]  Ashwin Srinivasan,et al.  Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001 , 2003, Bioinform..

[200]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[201]  Hannu Toivonen,et al.  Statistical evaluation of the predictive toxicology challenge , 2000 .

[202]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[203]  Vijay R. Konda,et al.  Actor-Critic Algorithms , 1999, NIPS.

[204]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[205]  A. Debnath,et al.  Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity. , 1991, Journal of medicinal chemistry.