A probabilistic training scheme for the time-concentration network
KBCS '89 Proceedings of the international conference on Knowledge based computer systems
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Relational Dependency Networks
The Journal of Machine Learning Research
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
An object-oriented representation for efficient reinforcement learning
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Proceedings of the 25th international conference on Machine learning
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
ECML '07 Proceedings of the 18th European conference on Machine Learning
Practical solution techniques for first-order MDPs
Artificial Intelligence
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Near-Bayesian exploration in polynomial time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Relevance Grounding for Planning in Relational Domains
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Efficient learning of action schemas and web-service descriptions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
FLUCAP: a heuristic search planner for first-order MDPs
Journal of Artificial Intelligence Research
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Efficient reinforcement learning in factored MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Top-down induction of first-order logical decision trees
Artificial Intelligence
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
Learning models of relational MDPs using graph kernels
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Exploring compact reinforcement-learning representations with linear regression
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A unifying framework for computational reinforcement learning theory
A unifying framework for computational reinforcement learning theory
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
Knows what it knows: a framework for self-aware learning
Machine Learning
Efficient learning of relational models for sequential decision making
Efficient learning of relational models for sequential decision making
An object-oriented representation for efficient reinforcement learning
An object-oriented representation for efficient reinforcement learning
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A fundamental problem in reinforcement learning is balancing exploration and exploitation. We address this problem in the context of model-based reinforcement learning in large stochastic relational domains by developing relational extensions of the concepts of the E3 and R-MAX algorithms. Efficient exploration in exponentially large state spaces needs to exploit the generalization of the learned model: what in a propositional setting would be considered a novel situation and worth exploration may in the relational setting be a well-known context in which exploitation is promising. To address this we introduce relational count functions which generalize the classical notion of state and action visitation counts. We provide guarantees on the exploration efficiency of our framework using count functions under the assumption that we had a relational KWIK learner and a near-optimal planner. We propose a concrete exploration algorithm which integrates a practically efficient probabilistic rule learner and a relational planner (for which there are no guarantees, however) and employs the contexts of learned relational rules as features to model the novelty of states and actions. Our results in noisy 3D simulated robot manipulation problems and in domains of the international planning competition demonstrate that our approach is more effective than existing propositional and factored exploration techniques.