UTCS Artificial Intelligence
courses
talks/events
demos
people
projects
publications
software/data
labs
areas
admin
Compositional Models for Reinforcement Learning (2009)
Nicholas K. Jong
and
Peter Stone
Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, but these three ideas have rarely been studied together. This paper develops a unified framework that formalizes these algorithmic contributions as operators on learned models of the environment. Our formalism reveals some synergies among these innovations, and it suggests a straightforward way to compose them. The resulting algorithm, Fitted R-MAXQ, is the first to combine the function approximation of fitted algorithms, the efficient model-based exploration of R-MAX, and the hierarchical decompostion of MAXQ.
View:
PDF
,
PS
,
HTML
Citation:
In
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
, September 2009.
Bibtex:
@InProceedings{ECML09-jong, title={Compositional Models for Reinforcement Learning}, author={Nicholas K. Jong and Peter Stone}, booktitle={The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases}, month={September}, url="http://www.cs.utexas.edu/users/ai-lab?ECML09-jong", year={2009} }
People
Nicholas Jong
Ph.D. Alumni
nickjong [at] me com
Peter Stone
Faculty
pstone [at] cs utexas edu
Areas of Interest
Machine Learning
Labs
Learning Agents