Inverse kinematics for manipulators using deep reinforcement learning

Inverzna kinematika manipulatora učenja iznimno je kompliciran problem. Cilj ovog rada je bio riješiti taj problem uz pomoć dubokog potpornog učenja. Napravljen je pregled područja dubokog potpornog učenja, iznesene su teoretske osnove, ključni pojmovi dubokog potpornog učenja te algoritmi gradijenta politike. Riješen je problem dohvaćanja objekta u prostoru korištenjem robotske ruke Jaco, prvo u prostoru bez prepreka, a zatim i s preprekama.Inverse kinematics of robotic manipulators is a very complicated problem. The aim of this thesis was to solve that problem using deep reinforcement learning. An overview of the area of deep reinforcement learning was made, the theoretical foundations, key concepts of deep supportive learning and policy gradient algorithms were presented. Problem of reaching target in space using robotic manipulator arm Jaco was solved, first in space without obstacles and then in space with obstacles