Strategy acquisition governed by experimentation
Selected and updated papers from the proceedings of the 1982 European conference on Progress in artificial intelligence
Learning by experimentation: the operator refinement method
Machine learning
Technical Note: \cal Q-Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Elements of machine learning
Machine Learning - Special issue on reinforcement learning
Machine Learning - special issue on inductive logic programming
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Machine Learning - Special issue on context sensitivity and concept drift
Learning Logical Definitions from Relations
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Logical Decision Trees for Clustering
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty
Artificial Intelligence Review
Learning to fly by combining reinforcement learning with behavioural cloning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
| Hi-index | 0.00 |
Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the effects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with reinforcement learning. In particular, relational reinforcement learning allows us to employ structural representations, to abstract from specific goals pursued and to exploit the results of previous learning phases when addressing new (more complex) situations.