Genetic policy search using exemplar based representations
نویسنده
چکیده
Direct policy search (DPS) is a method for machine learning, where the policy is represented by some parameters and they are optimized by evaluating the policy directly. We introduce exemplar based policy (EBP) optimization as a framework of DPS. An EBP is a policy composed of a set of exemplars which is parameter to be optimised, and a case-based action selector. An actual implementation with GA optimiser, genetic policy search, is introduced and its performances in two problem domains are shown.
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