Relational reinforcement learning
Machine Learning - Special issue on inducive 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
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
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and 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
Approximate inference for planning in stochastic relational worlds
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
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Active learning with statistical models
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
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Learning models of relational MDPs using graph kernels
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Efficient learning of relational models for sequential decision making
Efficient learning of relational models for sequential decision making
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
| Hi-index | 0.00 |
One of the key problems in model-based reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large relational domains, in which there is a varying number of objects and relations between them. We provide one of the first solutions to exploring large relational Markov decision processes by developing relational extensions of the concepts of the Explicit Explore or Exploit (E3) algorithm. A key insight is that the inherent generalization of learnt knowledge in the relational representation has profound implications also on the exploration strategy: what in a propositional setting would be considered a novel situation and worth exploration may in the relational setting be an instance of a well-known context in which exploitation is promising. Our experimental evaluation shows the effectiveness and benefit of relational exploration over several propositional benchmark approaches on noisy 3D simulated robot manipulation problems.