Relational Reinforcement Learning

Cited by (371)

Items 101–200 of 371

1. An Experiment in Robot Discovery with ILP
   Gregor Leban, Jure Žabkar, Ivan Bratko A · 2008 · Lecture notes in computer science
2. Relevance Grounding for Planning in Relational Domains
   Tobias Lang, Marc Toussaint · 2009 · Lecture notes in computer science
3. PDDLGym: Gym Environments from PDDL Problems
   Tom Silver, Rohan Chitnis · 2020 · arXiv (Cornell University)
4. Learning in Worlds with Objects
   Leslie Pack Kaelbling, Tim Oates, Natalia Hern, Sarah J. Finney · 2017 · Encyclopedia of Machine Learning and Data Mining
5. Two steps reinforcement learning
   Fernando Fernández, Daniel Borrajo · 2008 · International Journal of Intelligent Systems
6. A theory of relation learning and cross-domain generalization.
   Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin, John E. Hummel · 2022 · Psychological Review
7. Solving Relational and First-Order Logical Markov Decision Processes: A Survey
   Martijn van Otterlo · 2012 · Adaptation, learning, and optimization
8. INTEGRATING PLANNING, EXECUTION, AND LEARNING TO IMPROVE PLAN EXECUTION
   Sergio Jiménez, Fernando Fernández, Daniel Borrajo · 2012 · Computational Intelligence
9. Persistent rule-based interactive reinforcement learning
   Adam Bignold, Francisco Cruz, Richard Dazeley, Peter W. Vamplew, Cameron Foale · 2021 · Neural Computing and Applications
10. Incremental Learning of Relational Action Models in Noisy Environments
    Christophe Rodrigues, Pierre Gérard, Céline Rouveirol · 2011 · Lecture notes in computer science
11. Combining model-based and instance-based learning for first order regression
    Kurt Driessens, Sašo Džeroski · 2005 · International Conference on Machine Learning
12. An inductive logic programming approach to statistical relational learning
    Kristian Kersting B · 2006
13. On the numeric stability of Gaussian processes regression for relational reinforcement learning
    Jan Ramon, Kurt Driessens · 2004 · Lirias
14. Learning Multi-Agent Communication Through Structured Attentive Reasoning
    Murtaza Rangwala, Ryan K. Williams · 2020 · neural information processing systems
15. Efficient learning of relational models for sequential decision making
    Walsh, Thomas J. · 2010 · Rutgers University Community Repository (Rutgers University)
16. Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
    Titouan Vayer, Chapel, Laetita, Rémi Flamary, Romain Tavenard, Nicolas Courty · 2018 · arXiv (Cornell University)
17. Deepening the IDA* algorithm for knowledge graph reasoning through neural network architecture
    Qi Wang, Yongsheng Hao, Feng Chen · 2020 · Neurocomputing
18. Active Learning of Relational Action Models
    Christophe Rodrigues, Pierre Gérard, Céline Rouveirol, Henry Soldano · 2012 · Lecture notes in computer science
19. Personalisation for user agents
    J. J. Cole, M. J. Gray, John Wylie Lloyd, Kee Siong Ng · 2005 · Adaptive Agents and Multi-Agents Systems
20. RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction
    Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad V. Tadepalli · 2021 · Proceedings of the International Conference on Automated Planning and Scheduling
21. Learning the behavior model of a robot
    Guillaume Infantes, Malik Ghallab, Félix Ingrand · 2010 · Autonomous Robots
22. Logical Markov Decision Programs
    Kristian Kersting B, Luc De Raedt · 2003
23. A first order logic classifier system
    Drew Mellor · 2005 · Genetic and Evolutionary Computation Conference (GECCO)
24. Relational temporal difference learning
    Nima Asgharbeygi, David John Stracuzzi, Pat Langley B · 2006 · International Conference on Machine Learning
25. GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
    Rohan Chitnis, Tom Silver, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Tomás Lozano‐Pérez · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
26. SmartGantt – An interactive system for generating and updating rescheduling knowledge using relational abstractions
    Jorge Andrés Palombarini, Ernesto Carlos Martinez · 2012 · Computers & Chemical Engineering
27. Multi-agent Relational Reinforcement Learning
    Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice Bruynooghe · 2006 · Lecture notes in computer science
28. Hierarchical reinforcement learning : a hybrid approach
    Malcolm Ryan · 2022
29. Exploration in Relational Worlds
    Tobias Lang, Marc Toussaint, Kristian Kersting B · 2010 · Lecture notes in computer science
30. Novel design of reward and epsilon-greedy decay strategy tailored for Q-learning in optimizing local mobile robot path planning
    Marouane Ben-Akka, Camel Tanougast, Camille Diou · 2025 · Knowledge-Based Systems
31. Coupled learning of action parameters and forward models for manipulation
    Sebastian Höfer, Oliver Brock · 2016 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
32. Reducing Computational Cost During Robot Navigation and Human–Robot Interaction with a Human-Inspired Reinforcement Learning Architecture
    Rémi Dromnelle, Erwan Renaudo, Mohamed Chétouani, Petros A. Maragos, Raja Chatila, Benoît Girard, Mehdi Khamassi · 2022 · International Journal of Social Robotics
33. Allocating time and location information to activity–travel patterns through reinforcement learning
    Davy Janssens, Yu Lan, Geert Wets, Guoqing Chen · 2007 · Knowledge-Based Systems
34. A Factorization Approach to Manipulation in Unstructured Environments
    Dov Katz, Oliver Brock · 2011 · Springer tracts in advanced robotics
35. Autonomous inter-task transfer in reinforcement learning domains
    Matthew Edmund Taylor · 2007 · Texas ScholarWorks (Texas Digital Library)
36. Optimization-based approximate dynamic programming
    Shlomo Zilberstein B, Marek Petrik · 2010
37. Adaptive Modelling and Planning for Learning Intelligent Behaviour
    Mykel J. Kochenderfer · 2006 · ERA
38. Learning Models of Relational MDPs Using Graph Kernels
    Florian Halbritter, Peter Geibel · 2007 · Springer eBooks
39. Extracting kinematic background knowledge from interactions using task-sensitive relational learning
    Sebastian Höfer, Tobias Lang, Oliver Brock · 2014 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
40. Modelling Shared Attention Through Relational Reinforcement Learning
    Renato Ramos da Silva, Roseli Aparecida Francelin Romero · 2011 · Journal of Intelligent & Robotic Systems
41. Guiding Inference Through Relational Reinforcement Learning
    Nima Asgharbeygi, Negin Nejati, Pat Langley B, Sachiyo Arai · 2005 · Lecture notes in computer science
42. Statistical Relational Learning
    Hendrik Blockeel · 2013 · Intelligent systems reference library
43. Transfer learning for reinforcement learning through goal and policy parametrization
    Kurt Driessens, Jan Ramon, Tom Croonenborghs · 2006 · Lirias
44. Multistrategy Learning for Robot Behaviours
    Claude Sammut, Tak Fai Yik · 2009 · Studies in computational intelligence
45. Coevolution of pattern generators and recognizers
    Stewart W. Wilson · 2009 · Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
46. Towards Informed Reinforcement Learning
    Tom Croonenborghs, Jan Ramon, Maurice Bruynooghe · 2004 · Lirias
47. Discovering relational domain features for probabilistic planning
    Jia‐Hong Wu, Robert L. Givan · 2007
48. SRL2003 IJCAI 2003 Workshop on Learning Statistical Models from Relational Data
    Lise C. Getoor · 2003 · ScholarWorks@UMassAmherst (University of Massachusetts Amherst)
49. An Object-Oriented Approach to Reinforcement Learning in an Action Game
    Shiwali Mohan, John E. Laird · 2011 · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
50. Relational Reinforcement Learning for Agents in Worlds with Objects
    Sašo Džeroski · 2003 · Lecture notes in computer science
51. A Learning Classifier System Approach to Relational Reinforcement Learning
    Drew Mellor · 2008 · Lecture notes in computer science
52. Learning with Deictic Representation
    Sarah J. Finney, Natalia H. Gardiol, Leslie Pack Kaelbling, Tim Oates · 2002 · DSpace@MIT (Massachusetts Institute of Technology)
53. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction
    Ronald C. Taylor, Mudita Singhal, Don Simone Daly, Kelly Domico, Amanda M. White, Deanna L. Auberry, Kenneth J. Auberry, Brian S. Hooker, Gregory B. Hurst, Jason McDermott et al. · 2007 · International Conference on Machine Learning and Applications
54. Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies
    Rémi Dromnelle, Benoît Girard, Erwan Renaudo, Raja Chatila, Mehdi Khamassi · 2020 · IEEE RO-MAN
55. Reinforcement Learning as Heuristic for Action-Rule Preferences
    Joost Broekens, Koen V. Hindriks, Pascal Wiggers · 2012 · Lecture notes in computer science
56. Relational knowledge with predictive state representations
    David Wingate, Vishal Dineshkumar Soni, Britton Wolfe, Satinder Pal Singh · 2007
57. Utile distinctions for relational reinforcement learning
    William Dabney, Amy McGovern · 2007
58. Learning Comprehensible Theories from Structured Data
    Kee Siong Ng · 2005 · ANU Open Research (Australian National University)
59. Relational Reinforcement Learning
    Kurt Driessens · 2016 · Encyclopedia of Machine Learning and Data Mining
60. Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming
    Ali Payani, Faramarz Fekri · 2020 · arXiv (Cornell University)
61. A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making
    Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares · 2024 · ACM Computing Surveys
62. Entity-based Reinforcement Learning for Autonomous Cyber Defence
    Isaac Thompson, Alberto Caron, Chris Hicks, Vasilios Mavroudis · 2023
63. Relational Representations in Reinforcement Learning: Review and Open Problems
    Martijn van Otterlo · 2002 · University of Twente Research Information
64. Learning to Improve Agent Behaviours in GOAL
    Dhirendra Singh, Koen V. Hindriks · 2013 · Lecture notes in computer science
65. Probabilistic relational planning with first order decision diagrams
    Saket Joshi, Roni Khardon · 2011
66. Efficient Spatiotemporal Transformer for Robotic Reinforcement Learning
    Yiming Yang, Dengpeng Xing, Bo Xu · 2022 · IEEE Robotics and Automation Letters
67. Predictive Graph Mining with Kernel Methods
    Thomas Gärtner · 2005 · Springer eBooks
68. Reinforcement learning via approximation of the Q-function
    Marina Langlois, Robert H. Sloan · 2010 · Journal of Experimental & Theoretical Artificial Intelligence
69. Apply ant colony optimization to Tetris
    Xingguo Chen, Hao Wang, Weiwei Wang, Yinghuan Shi, Yang Gao · 2009 · Genetic and Evolutionary Computation Conference (GECCO)
70. Building Relational World Models for Reinforcement Learning
    Trevor Walker, Lisa A. Torrey, JUDE W. SHAVLIK, Richard Maclin · 2008 · Springer eBooks
71. Relational reinforcement learning applied to shared attention
    Renato R. da Silva, Claudio A. Policastro, Roseli Aparecida Francelin Romero · 2009 · International Joint Conference on Neural Networks (IJCNN)
72. Combining strategies efficiently: high-quality decisions from conflicting advice
    Wouter Michiel Koolen · 2011 · UvA-DARE (University of Amsterdam)
73. Automatic discretization of actions and states in Monte-Carlo tree search
    Guy Van den Broeck, Kurt Driessens · 2011 · Lirias
74. CARL: A Synergistic Framework for Causal Reinforcement Learning
    Arquímides Méndez-Molina, Eduardo F. Morales, Luis Enrique Sucar · 2023 · IEEE Access
75. Learning with Configurable Operators and RL-Based Heuristics
    Fernando Martínez‐Plumed, Cèsar Ferri, José Hernández‐Orallo, Marïa José Ramírez-Quintana · 2013 · Lecture notes in computer science
76. Planning in hybrid relational MDPs
    Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt · 2017 · Machine Learning
77. Learning in Clausal Logic: A Perspective on Inductive Logic Programming
    Peter A. Flach, Nada Lavrač A · 2002 · Lecture notes in computer science
78. Object-Oriented Representation and Hierarchical Reinforcement Learning in Infinite Mario
    M. Joshi, Rakesh Khobragade, S. Sarda, Umesh Ashok Deshpande, Shiwali Mohan · 2012 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
79. Automated domain analysis and transfer learning in general game playing
    Gregory Kuhlmann · 2010
80. Towards Interpretable Reinforcement Learning with State Abstraction Driven by External Knowledge
    Nicolas Bougie, Ryutaro Ichise · 2020 · IEICE Transactions on Information and Systems
81. Compact Representation of Knowledge Bases in ILP
    Jan Struyf, Jan Ramon, Hendrik Blockeel · 2003 · Lecture notes in computer science
82. Learning to transfer optimal navigation policies
    Kristian Kersting B, Christian Plagemann, Alexandru Cocora, Wolfram Burgard, Luc De Raedt · 2007 · Advanced Robotics
83. A contribution to Optimal Transport on incomparable spaces
    Titouan Vayer · 2020 · arXiv (Cornell University)
84. Differentiable Logic Machines
    Matthieu Zimmer, Xuening Feng, Claire Glanois, Zhaohui Jiang, Jianyi Zhang, Paul Weng, Dong Li, Jianye Hao, Wulong Liu · 2021 · arXiv (Cornell University)
85. Bidirectional Q-learning for recycling path planning of used appliances under strong and weak constraints
    Qi Yang, Jin Xin Cao, Baijing Wu · 2024 · Communications in Transportation Research
86. Intelligent Object Exploration
    Robert Gaschler, Dov Katz, Martin Grund, Peter A., Oliver Brock · 2012 · InTech eBooks
87. Online Feature Discovery in Relational Reinforcement Learning
    Scott Sanner · 2006
88. Model-assisted approaches for relational reinforcement learning
    Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe · 2007 · Lirias
89. Transferable XCS
    Xianneng Li, Guangfei Yang · 2016 · Genetic and Evolutionary Computation Conference (GECCO)
90. Model-assisted approaches for relational reinforcement learning: Some challenges for the SRL community
    Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe · 2006 · Lirias
91. Learning with Whom to Communicate Using Relational Reinforcement Learning
    Marc Ponsen, Tom Croonenborghs, Karl Tuyls, Jan Ramon, Kurt Driessens, H.J. van den Herik, Eric O. Postma · 2010 · Studies in computational intelligence
92. Strategy Entropy as a Measure of Strategy Convergence in Reinforcement Learning
    Xiaodong Zhuang, Zhuo Chen · 2008 · International Conference on Intelligent Networks and Intelligent Systems
93. Jason Smiles: Incremental BDI MAS Learning
    Alejandro Guerra‐Hernández, Gustavo Ortiz Hernández, Wulfrano Arturo Luna-Ramírez · 2007 · Mexican International Conference on Artificial Intelligence
94. Learning domain-specific planners from example plans
    Manuela Veloso, Elly Winner · 2008
95. Acquiring Domain-Specific Planners by Example
    Elly Winner, Manuel Veloso · 2003
96. Interpretable UAV Collision Avoidance using Deep Reinforcement Learning
    Deepak-George Thomas, Daniil Olshanskyi, Karter Krueger, Wongpiromsarn, Tichakorn, Jannesari, Ali · 2021 · arXiv (Cornell University)
97. Explanatory machine learning for sequential human teaching
    Lun Ai, Johannes Langer, Stephen Muggleton, Ute Schmid · 2023 · Machine Learning
98. A Round-robin Scheduling Algorithm of Relay-nodes in WSN Based on Self-adaptive Weighted Learning for Environment Monitoring
    Liu Zhang, Feihu Chen · 2014 · Journal of Computers
99. Concept Learning By Human Tutelage For Social Robots
    Renato Ramos da Silva, Claudio A. Policastro, Giovana Zuliani, Ednaldo Brigante Pizzolato, Roseli Aparecida Francelin Romero · 2008 · Learning and Nonlinear Models
100. A Review of Domain Knowledge Representation for Robot Task Planning
     Xiaolei Sun, Yu Zhang · 2019 · International Conference on Mathematics and Artificial Intelligence (ICMAI)