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ICML
2004
IEEE

Using relative novelty to identify useful temporal abstractions in reinforcement learning

14 years 10 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 Science, University of Massachusetts, Amherst, MA 01003-9264 We present a new method for automatically creating useful temporal abstractions in reinforcement learning. We argue that states that allow the agent to transition to a different region of the state space are useful subgoals, and propose a method for identifying them using the concept of relative novelty. When such a state is identified, a temporallyextended activity (e.g., an option) is generated that takes the agent efficiently to this state. We illustrate the utility of the method in a number of tasks.
Özgür Simsek, Andrew G. Barto
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Özgür Simsek, Andrew G. Barto
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