Sciweavers

COR
2008
142views more  COR 2008»
13 years 11 months ago
Application of reinforcement learning to the game of Othello
Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such c...
Nees Jan van Eck, Michiel C. van Wezel
NIPS
2003
14 years 26 days ago
Envelope-based Planning in Relational MDPs
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making d...
Natalia Hernandez-Gardiol, Leslie Pack Kaelbling
KR
1989
Springer
14 years 3 months ago
Situated Control Rules
In this work we extend the work of Dean, Kaelbling, Kirman and Nicholson on planning under time constraints in stochastic domains to handle more complicated scheduling problems. I...
Mark Drummond
APN
1999
Springer
14 years 3 months ago
Autonomous Continuous P/T Systems
Discrete event dynamic systems may have extremely large state spaces. For their analysis, it is usual to relax the description by removing the integrality constraints. Applying thi...
Laura Recalde, Enrique Teruel, Manuel Silva
SPIN
2009
Springer
14 years 6 months ago
Fast, All-Purpose State Storage
Existing techniques for approximate storage of visited states in a model checker are too special-purpose and too DRAM-intensive. Bitstate hashing, based on Bloom filters, is good ...
Peter C. Dillinger, Panagiotis Manolios
ICML
2005
IEEE
15 years 9 days ago
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
MDPs are an attractive formalization for planning, but realistic problems often have intractably large state spaces. When we only need a partial policy to get from a fixed start s...
H. Brendan McMahan, Maxim Likhachev, Geoffrey J. G...
ICML
2008
IEEE
15 years 9 days ago
An object-oriented representation for efficient reinforcement learning
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Carlos Diuk, Andre Cohen, Michael L. Littman