Batch mode reinforcement learning based on the synthesis of artificial trajectories

Raphael Fonteneau (), Susan Murphy (), Louis Wehenkel () and Damien Ernst ()

Annals of Operations Research, 2013, vol. 208, issue 1, 383-416

Abstract: In this paper, we consider the batch mode reinforcement learning setting, where the central problem is to learn from a sample of trajectories a policy that satisfies or optimizes a performance criterion. We focus on the continuous state space case for which usual resolution schemes rely on function approximators either to represent the underlying control problem or to represent its value function. As an alternative to the use of function approximators, we rely on the synthesis of “artificial trajectories” from the given sample of trajectories, and show that this idea opens new avenues for designing and analyzing algorithms for batch mode reinforcement learning. Copyright Springer Science+Business Media New York 2013

Keywords: Reinforcement learning; Optimal control; Artificial trajectories; Function approximators (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-012-1248-5

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