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ICML
2004
IEEE
16 years 19 days ago
Using relative novelty to identify useful temporal abstractions in reinforcement learning
lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
Özgür Simsek, Andrew G. Barto
NIPS
1997
15 years 1 months ago
Nonparametric Model-Based Reinforcement Learning
This paper describes some of the interactions of model learning algorithms and planning algorithms we have found in exploring model-based reinforcement learning. The paper focuses...
Christopher G. Atkeson
ICML
2006
IEEE
16 years 19 days ago
Autonomous shaping: knowledge transfer in reinforcement learning
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that est...
George Konidaris, Andrew G. Barto
ICML
2005
IEEE
16 years 19 days ago
Learning strategies for story comprehension: a reinforcement learning approach
This paper describes the use of machine learning to improve the performance of natural language question answering systems. We present a model for improving story comprehension th...
Eugene Grois, David C. Wilkins
ATAL
2009
Springer
15 years 6 months ago
SarsaLandmark: an algorithm for learning in POMDPs with landmarks
Reinforcement learning algorithms that use eligibility traces, such as Sarsa(λ), have been empirically shown to be effective in learning good estimated-state-based policies in pa...
Michael R. James, Satinder P. Singh