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ICML
2003
IEEE

Action Elimination and Stopping Conditions for Reinforcement Learning

14 years 11 months ago
Action Elimination and Stopping Conditions for Reinforcement Learning
We consider incorporating action elimination procedures in reinforcement learning algorithms. We suggest a framework that is based on learning an upper and a lower estimates of the value function or the Q-function and eliminating actions that are not optimal. We provide a model-based and a model-free variants of the elimination method. We further derive stopping conditions that guarantee that the learned policy is approximately optimal with high probability. Simulations demonstrate a considerable speedup and added robustness.
Eyal Even-Dar, Shie Mannor, Yishay Mansour
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2003
Where ICML
Authors Eyal Even-Dar, Shie Mannor, Yishay Mansour
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