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this is self learning robot using reinforcement learning (Q-learning, Watkins 1989); it learns to choose actions from state rewards - no other support controller required (PID, Adaptive system ...) to store Q values for small state space table can be used; for large state space some approximation is necessary - Iam using assocciative neural network; state for line following test are three last line possitions S(n) = (L(n), L(n-1), L(n-2)), where each L can have 128 possible line possitions : 128^3 ...
This is a companion discussion topic for the original entry at https://community.robotshop.com/index.php/robots/show/self-learning-robot