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Heretofore, the mo st presented L earning Automata (LA) is invented to interact with double level environments (one level for reward and the other for penalty). Those LA are often expedient, optimal or both of them and can minimize their mean value of receiving penalties (or at least converge to the minimum point) during time an d work much better than a pure-chance automaton. However, in m any operational...
In most articles about machine learning, particularly in reinforcement learning, a learning system interacts with an unknown environment and tries to improve its performance (receiving fewer penalties) according to feedback from environment as reinforcement signals. In this paper we show how a learning system can learn to interact with environment from an experienced agent (experienced learning system)...
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