Reinforcement learning and

dynamic programming using function approximators Preface Control systems are making a tremendous impact on our society. Though invisible to most users, they are essential for the operation of nearly all devices – from basic home appliances to aircraft and nuclear power plants. Apart from technical systems, the principles of control are routinely applied and exploited in a variety of disciplines such as economics, medicine, social sciences, and artificial intelligence. A common denominator in the diverse applications of control is the need to in-fluence or modify the behavior of dynamic systems to attain prespecified goals. One approach to achieve this is to assign a numerical performance index to each state tra-jectory of the system. The control problem is then solved by searching for a control policy that drives the system along trajectories corresponding to the best value of the performance index. This approach essentially reduces the problem of finding good control policies to the search for solutions of a mathematical optimization problem