A Michigan style architecture for learning finite state controllers: a first step
نویسندگان
چکیده
In this paper, we describe FACS, a new Michigan style architecture able to build Finite-State Automata controllers for agents learning to solve nonMarkov problems. FACS relies on a population of partial automata and implements a Reinforcement Learning algorithm to compute the strength of each automaton and a Genetic Algorithm to select and discover efficient automata. We detail our approach and present very preliminary results.
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