Symbolic method for deriving policy in reinforcement learning

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@InProceedings{Alibekov:2016:CDC,

• author = "Eduard Alibekov and Jiri Kubalik and Robert Babuska",
• booktitle = "2016 IEEE 55th Conference on Decision and Control (CDC)",
• title = "Symbolic method for deriving policy in reinforcement learning",
• year = "2016",
• pages = "2789--2795",
• abstract = "This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The symbolic proxy function is constructed such that it maximizes the number of correct choices of the control input for a set of selected states. Maximization methods can then be used to derive a control policy that performs better than the policy derived from the original approximate value function. The method was experimentally evaluated on two control problems with continuous spaces, pendulum swing-up and magnetic manipulation, and compared to a standard policy derivation method using the value function approximation. The results show that the proposed method and its variants outperform the standard method.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CDC.2016.7798684",
• month = dec,
• notes = "Also known as \cite{7798684}",

}

Genetic Programming entries for Eduard Alibekov Jiri Kubalik Robert Babuska

Citations