self learning robot

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self learning robot

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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 ...

self learning robot
self learning robot

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 memory places for storing in table - too much ^_^ with using pure table, using assocciative neural network - no problem

software is written in Atom text editor, compiled with arm-none-eabi-g++, for flashing Iam using dfu-util (mcu has USB bootloader in ROM), all program is running on robot, result data are transmitted via USART

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self learning robot

self learning robot

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