| 1.Relational
Reinforcement Learning: An Overview |
| Prasad Tadepalli, Robert Givan, and Kurt Driessens |
| 2.On
the Numeric Stability of Gaussian Processes Regression for Relational
Reinforcement Learning |
| Jan Ramon and Kurt Driessens |
| 3.Relational
Reinforcement Learning via Sampling the Space of First-Order Conjunctive
Features |
| Trevor Walker, Jude Shavlik, and Richard
Maclin |
| 4.Towards
Informed Reinforcement Learning |
| Tom Croonenborghs, Jan Ramon, and Maurice Bruynooghe |
| 5.Relational
State Abstractions for Reinforcement Learning |
| Eduardo F. Morales |
| 6.Relational
Reinforcement Learning for Classical Planning |
| Alan Fern, SungWook Yoon, and Robert Givan |
| 7.Towards
Learning to Learn and Plan by Relational Reinforcement Learning |
| Hideaki Itoh and Kiyohiko Nakamura |
| 8.Relational
Spatial Features in Reinforcement Learning of Multi-Agent Search
Strategies |
| Malcolm Strens |
| 9.Abstract:
Generalization in Relational Reinforcement Learning |
| Aaron Wilson |
| 10.Exploiting
First-Order Regression in Inductive Policy Selection |
| Charles Gretton and Sylvie Thiebaux |
| 11.Soar-RL:
Integrating Reinforcement Learning with Soar |
| Shelley Nason and John E. Laird |
| 12.Model-Based
Learning with Hierarchical Relational Skills |
| Pat Langley, Sachiyo Arai, and Daniel Shapiro |
| 13.Function
Approximation in Hierarchical Relational Reinforcement Learning |
| Silvana Roncagliolo and Prasad Tadepalli |
| 14.Challenges
for Relational Reinforcement Learning |
| Martijn Van Otterlo and Kristian Kersting |