We present a neurobiologically motivated model for an agent which generates a representation of its spacial environment by an active exploration. Our main objectives is the introduction of an action-selection mechanism based on the principle of self-reinforcement learning, We introduce the action-selection mechanism under the constraint that the agent receives only information an animal could receive too. Hence, we have to avoid all supervised learning methods which require a teacher. To solve this problem, we define a self-reinforcement signal as qualitative comparison between predicted an perceived stimulus of the agent, The self-reinforcement signal is used to construct internally a self-punishment function and the agent chooses its actions to minimize this function during learning. As a result it turns out that an active action-selection mechanism can improve the performance significantly if the problem to be learned becomes more difficult. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Emmert-Streib, F. (2005). A neurobiologically motivated model for self-organized learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3789 LNAI, pp. 415–424). https://doi.org/10.1007/11579427_42
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