Relational Reinforcement Learning An Overview

By Prasad Tadepalli, Robert Givan. 2004

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Karl Tuyls
2. Sean Luke
3. Liviu Panait
4. Pieter Jan't Hoen
5. Srijita Das
6. Kaushik Roy
7. Oliver Brock
8. Han La Poutré

Citations in the corpus

• Fitted Q-Learning for Relational Domains (2020, background): “…worlds, and methods that directly learn and reason at the first-order level have been developed in the broad area of Relational Reinforcement Learning (RRL) (Price and Boutilier 2001; Sanner and Boutilier 2009; Wang, Joshi, and Khardon 2008; Tadepalli, Givan, and Driessens 2004; Wu and Givan 2007).”
• An Overview of Cooperative and Competitive Multiagent Learning (2005, methodology): “One such succesfull method in singleagent learning has been the cross fertilization between reinforcement learning and inductive logic programming [41, 39, 168].”
• Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation (2008, background): “tal acquisition of kinematic representations of objects as a relational reinforcement learning [7, 25, 27] problem.”