2012 | OriginalPaper | Buchkapitel
Monte-Carlo Swarm Policy Search
verfasst von : Jeremy Fix, Matthieu Geist
Erschienen in: Swarm and Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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Finding optimal controllers of stochastic systems is a particularly challenging problem tackled by the optimal control and reinforcement learning communities. A classic paradigm for handling such problems is provided by Markov Decision Processes. However, the resulting underlying optimization problem is difficult to solve. In this paper, we explore the possible use of Particle Swarm Optimization to learn optimal controllers and show through some non-trivial experiments that it is a particularly promising lead.