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    Lester Ingber Research

    A Reinforcement Learning Method Based on Adaptive Simulated Annealing

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    Reinforcement learning is a hard problem and the majority of the existing algorithms suffer from poor convergence properties for difficult problems. In this paper we propose a new reinforcement learning method that utilizes the power of global optimization methods such as simulated annealing. Specifically, we use a particularly powerful version of simulated annealing called adaptive simulated annealing (ASA) (Ingber, 1989). Towards this end we consider a batch formulation for the reinforcement learning problem, unlike the online formulation almost always used. The advantage of the batch formulation is that it allows state-of-the-art optimization procedures to be employed, and thus can lead to further improvements in algorithmic convergence properties. The proposed algorithm is applied to a decision making test problem, and it is shown to obtain better results than the conventional Q-learning algorithm.

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    Description

    Title : A Reinforcement Learning Method Based on Adaptive Simulated Annealing
    Author(s) : A.F. Atiya, A.G. Parlos, L. Ingber
    Abstract : Reinforcement learning is a hard problem and the majority of the existing algorithms suffer from poor convergence properties for difficult problems. In this paper we propose a new reinforcement learning method that utilizes the power of global optimization methods such as simulated annealing. Specifically, we use a particularly powerful version of simulated annealing called adaptive simulated annealing (ASA) (Ingber, 1989). Towards this end we consider a batch formulation for the reinforcement learning problem, unlike the online formulation almost always used. The advantage of the batch formulation is that it allows state-of-the-art optimization procedures to be employed, and thus can lead to further improvements in algorithmic convergence properties. The proposed algorithm is applied to a decision making test problem, and it is shown to obtain better results than the conventional Q-learning algorithm.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2003

    Affiliations Lester Ingber Research
    Journal : 2003 46th Midwest Symposium on Circuits and Systems
    Volume : 1
    Issue : x
    Publisher : IEEE
    Pages : 121 - 124
    Url : http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1562233
    Isbn : 0-7803-8294-3
    Doi : 10.1109/MWSCAS.2003.1562233

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