An open problem in hierarchical reinforcement learning is how to automatically generate hierarchies, e.g. options. We consider an immune clustering approach for automatic construction of options in a dynamic environment. The learning agent generates an undirected edge-weighted topological graph of the environment state transitions online. An immune clustering algorithm is then used to partition the state space. A second immune response algorithm is used to update the clusters when a new state being encountered later. Local strategies for reaching the different parts of the space are separately learned and added to the model in a form of options. By our approach, the options not only can be automatically generated but also can be dynamically updated.


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    Title :

    Automatic option generation in hierarchical reinforcement learning via immune clustering


    Contributors:
    Jing Shen, (author) / Guochang Gu, (author) / Haibo Liu, (author)


    Publication date :

    2006-01-01


    Size :

    2823841 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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