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Conference Papers Year : 2011

Transfer from Multiple MDPs

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Abstract

Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer samples from source tasks and include them in the training set used to solve a target task. In this paper, we investigate the theoretical properties of this transfer method and we introduce novel algorithms adapting the transfer process on the basis of the similarity between source and target tasks. Finally, we report illustrative experimental results in a continuous chain problem.
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Dates and versions

hal-00772620 , version 1 (10-01-2013)

Identifiers

  • HAL Id : hal-00772620 , version 1

Cite

Alessandro Lazaric, Marcello Restelli. Transfer from Multiple MDPs. NIPS - Twenty-Fifth Annual Conference on Neural Information Processing Systems, Dec 2011, Granada, Spain. ⟨hal-00772620⟩
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