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ICML
2003
IEEE

Action Elimination and Stopping Conditions for Reinforcement Learning

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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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