Exploiting Training Regimens to Improve Learning
نویسندگان
چکیده
While space does not permit a complete discussion, there are a variety of methods for leveraging humans for teaching machine agents. One common method is demonstration, where solutions to example problems are shown to the learner. Other methods have humans decompose problems so the learner need only solve a series of small problems (e.g., hierarchical decompositions (Dietterich, 1998), goalbased decomposition (Karlsson, 1997), or explicit training of skills (Stanley et al., 2005)). Still other methods, like reward shaping (Dorigo & Colombetti, 1994) leverage human input to guide agent exploration.
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