We present a perspective and challenges for Relational Reinforcement Learning (RRL). We first survey existing work and distinguish a number of main directions. We then highlight some research problems that are intrinsically involved in abstracting over relational Markov Decision Processes. These are the challenges of RRL. In addition, we describe a number of issues that will be important for further research into RRL. These are the challenges for RRL and deal with newly arising issues because of relational abstraction.
|Title of host publication||Proceedings of the Workshop on Relational Reinforcement Learning at ICML'04|
|Editors||P. Tadepalli, R Givan, K. Driessens|
|Place of Publication||Corvallis|
|Publisher||University of Alberta|
|Number of pages||7|
|Publication status||Published - 8 Jul 2004|
|Event||Workshop on Relational Reinforcement Learning 2004 - Banff, Canada|
Duration: 4 Jul 2004 → 8 Jul 2004
|Workshop||Workshop on Relational Reinforcement Learning 2004|
|Period||4/07/04 → 8/07/04|
- HMI-IA: Intelligent Agents
van Otterlo, M., & Kersting, K. (2004). Challenges for Relational Reinforcement Learning. In P. Tadepalli, R. Givan, & K. Driessens (Eds.), Proceedings of the Workshop on Relational Reinforcement Learning at ICML'04 (pp. 74-80). Corvallis: University of Alberta.