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This paper provides an overview of the reinforcementlearning and optimaladaptive control literature and its application to robotics. Reinforcementlearning is bridging the gap between traditional optimal control, adaptive control and bio-inspired learning techniques borrowed from animals. This work is highlighting some of the key techniques presented by well known researchers from the combined areas of reinforcementlearning and optimal control theory. At the end, an example of an implementation of a novel model-free Q-learning based discrete optimaladaptive controller for a humanoid robot arm is presented. The controller uses a novel adaptive dynamic programming (ADP) reinforcementlearning (RL) approach to develop an optimal policy on-line. The RL joint space tracking controller was implemented for two links (shoulder flexion and elbow flexion joints) of the arm of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) torso. The constrained case (joint limits) of the RL scheme was tested for a single link (elbow flexion) of the BERT II arm by modifying the cost function to deal with the extra nonlinearity due to the joint constraints.
|Translated title of the contribution||Reinforcement Learning and Optimal Adaptive Control: An Overview and Implementation examples|
|Pages (from-to)||42 - 59|
|Number of pages||18|
|Journal||Annual Reviews on Control|
|Publication status||Published - Apr 2012|