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ICMLA
2009
14 years 10 months ago
Automatic Feature Selection for Model-Based Reinforcement Learning in Factored MDPs
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...
Mark Kroon, Shimon Whiteson
JACIII
2006
97views more  JACIII 2006»
15 years 12 days ago
Opposition-Based Reinforcement Learning
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropri...
Hamid R. Tizhoosh
ICML
2004
IEEE
16 years 1 months 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
95
Voted
ML
1998
ACM
101views Machine Learning» more  ML 1998»
15 years 3 days ago
Elevator Group Control Using Multiple Reinforcement Learning Agents
Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an ...
Robert H. Crites, Andrew G. Barto
NIPS
1993
15 years 1 months ago
Robust Reinforcement Learning in Motion Planning
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...