The book bs with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. 
offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. 

The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. 

Readers will also enjoy:  

• A thorough introduction to model-based human-robot interaction control 
  
  
• Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles 
  
  
• Practical discussions of reinforcement learning for robot position and force control, as well as continuous reinforcement learning for robot force control 
  
  
• In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning  
  
  

Perfect for senior undergraduate and graduate students, acad researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, 
 is also an indispensable resource for students and professionals studying reinforcement learning.



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