Error Reducing Sampling in Reinforcement Learning

Bruno Scherrer 1 Shie Mannor 2
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In reinforcement learning, an agent collects information interacting with an environment and uses it to derive a behavior. This paper focuses on efficient sampling; that is, the problem of choosing the interaction samples so that the corresponding behavior tends quickly to the optimal behavior. Our main result is a sensitivity analysis relating the choice of sampling any state-action pair to the decrease of an error bound on the optimal solution. We derive two new model-based algorithms. Simulations demonstrate a quicker convergence (in the sense of the number of samples) of the value function to the real optimal value function.
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Submitted on : Friday, November 7, 2008 - 3:47:01 PM
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Bruno Scherrer, Shie Mannor. Error Reducing Sampling in Reinforcement Learning. NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning, Dec 2008, Whistler, Canada. ⟨inria-00337659⟩

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