Connectionists: Relational RL Survey

[Martijn van Otterlo]

Dear reader,

A comprehensive survey of relational reinforcement learning is available 
from my webpage:

"A Survey of Reinforcement Learning in Relational Domains".
M. van Otterlo -- TR-CTIT-05-31 - (70pp)
CTIT Technical Report Series ISSN 1381-3625

Abstract.
Reinforcement learning has developed into a primary approach for 
learning control strategies for autonomous agents. However, most of the 
work has focused on the algorithmic aspect, i.e. various ways of 
computing value functions and policies. Usually the representational 
aspects were limited to the use of attribute-value or propositional 
languages to describe states, actions etc. A recent direction -- under 
the general name of relational reinforcement learning -- is concerned 
with upgrading the representation of reinforcement learning methods to 
the first-order case, being able to speak, reason and learn about 
objects and relations between objects. This survey aims at presenting an 
introduction to this new field, starting from the classical 
reinforcement learning framework. We will describe the main motivations 
and challenges, and give a comprehensive survey of methods that have 
been proposed in the literature. The aim is to give a complete survey of 
the available literature, of the underlying motivations and of the 
implications of the new methods for learning in large, relational and 
probabilistic environments.

Work is underway to provide an updated version soon. Any comments, 
suggestions, and pointers to (new) work that does not yet appear in this 
survey will be greatly appreciated.

Regards,
Martijn van Otterlo.
http://www.cs.utwente.nl/~otterlo/