| 2008 |
|---|
| 11 | EE | Nicholas K. Jong,
Todd Hester,
Peter Stone:
The utility of temporal abstraction in reinforcement learning.
AAMAS (1) 2008: 299-306 |
| 10 | EE | Nicholas K. Jong:
Automatic induction of generalization hierarchies for reinforcement learning.
AAMAS (PhD) 2008: 1740-1741 |
| 9 | EE | Matthew E. Taylor,
Nicholas K. Jong,
Peter Stone:
Transferring Instances for Model-Based Reinforcement Learning.
ECML/PKDD (2) 2008: 488-505 |
| 2007 |
|---|
| 8 | EE | Nicholas K. Jong,
Peter Stone:
Model-based function approximation in reinforcement learning.
AAMAS 2007: 95 |
| 7 | EE | Nicholas K. Jong,
Peter Stone:
Model-Based Exploration in Continuous State Spaces.
SARA 2007: 258-272 |
| 6 | EE | Gerald Tesauro,
Nicholas K. Jong,
Rajarshi Das,
Mohamed N. Bennani:
On the use of hybrid reinforcement learning for autonomic resource allocation.
Cluster Computing 10(3): 287-299 (2007) |
| 2006 |
|---|
| 5 | EE | Gerald Tesauro,
Nicholas K. Jong,
Rajarshi Das,
Mohamed N. Bennani:
Improvement of Systems Management Policies Using Hybrid Reinforcement Learning.
ECML 2006: 783-791 |
| 4 | EE | Peter Stone,
Mohan Sridharan,
Daniel Stronger,
Gregory Kuhlmann,
Nate Kohl,
Peggy Fidelman,
Nicholas K. Jong:
From pixels to multi-robot decision-making: A study in uncertainty.
Robotics and Autonomous Systems 54(11): 933-943 (2006) |
| 2005 |
|---|
| 3 | | Patrick Beeson,
Nicholas K. Jong,
Benjamin Kuipers:
Towards Autonomous Topological Place Detection Using the Extended Voronoi Graph.
ICRA 2005: 4373-4379 |
| 2 | EE | Nicholas K. Jong,
Peter Stone:
State Abstraction Discovery from Irrelevant State Variables.
IJCAI 2005: 752-757 |
| 2003 |
|---|
| 1 | | Satinder P. Singh,
Michael L. Littman,
Nicholas K. Jong,
David Pardoe,
Peter Stone:
Learning Predictive State Representations.
ICML 2003: 712-719 |
