Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Learning by observation and practice: a framework for automatic acquisition of planning operators
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Logical settings for concept-learning
Artificial Intelligence
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Using background knowledge to speed reinforcement learning in physical agents
Proceedings of the fifth international conference on Autonomous agents
A polynomial time computable metric between point sets
Acta Informatica
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Distance based approaches to relational learning and clustering
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Why Experimentation can be better than "Perfect Guidance"
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Relational Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Least-Squares Methods in Reinforcement Learning for Control
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
A Framework for Behavioural Cloning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Thesis: relational reinforcement learning
AI Communications
Combining model-based and instance-based learning for first order regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Introduction and control of subgoals in reinforcement learning
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Proceedings of the 25th international conference on Machine learning
Dynamic and Neuro-Dynamic Optimization of a Fed-Batch Fermentation Process
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Learning relational options for inductive transfer in relational reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Exploration in relational worlds
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
Knowledge of opposite actions for reinforcement learning
Applied Soft Computing
Probabilistic relational planning with first order decision diagrams
Journal of Artificial Intelligence Research
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Multi-agent relational reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Safe exploration of state and action spaces in reinforcement learning
Journal of Artificial Intelligence Research
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Reinforcement learning, and Q-learning in particular, encounter two major problems when dealing with large state spaces. First, learning the Q-function in tabular form may be infeasible because of the excessive amount of memory needed to store the table, and because the Q-function only converges after each state has been visited multiple times. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. The first problem is often solved by learning a generalization of the encountered examples (e.g., using a neural net or decision tree). Relational reinforcement learning (RRL) is such an approach; it makes Q-learning feasible in structural domains by incorporating a relational learner into Q-learning. The problem of sparse rewards has not been addressed for RRL. This paper presents a solution based on the use of “reasonable policies” to provide guidance. Different types of policies and different strategies to supply guidance through these policies are discussed and evaluated experimentally in several relational domains to show the merits of the approach.