Seite - 116 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources

4. ControlSystemDesign or 4.42 . In other words, any control policy that is greedy [ZSWM00] [RS07] with respect to the optimal state-action value function 4.42 is anoptimalpolicy [Bar98], suchas pi∗(s,u) : u= u∗= argmax u Q∗(s,u), (4.43) whereu∗ indicates theoptimalcontrolaction. Modelof theplant The function of the model is to mimic the dynamics of the plant and predict future states and rewards. It is not a compulsory element for all RLC methods. Classical RLC methods use the pure trial-and-error strategy, where all of their learning and approximations are based on explicitly experienced interactions, and the model of the plant is not needed. InmodernRLCmethods,planningbasedonthemodelof the plant is often utilized to speed up the learning process [Die99]. Be- sides real experiences, simulated experiences from the model of the plant is also useful to improve and update the control policy [AR01]. AlthoughRLCwiththeincremental/onlineplanningcostsmorecom- putation power than the direct RLC, it showed in [AS97] and [LV09] that a faster learning speed and a higher expected return can be ob- tainedbecauseof the involvementofexplicitmodels. Several points are worthy of note regarding these four parts. Firstly, RL is able to deal with different types of problems and control tasks, as longasthevaluefunctionisclearlydefinedandrewardsareappro- priately assigned. From this point of view, the task (equation 4.30) canbesolvedusingRL.Secondly, Inpracticalcontrolapplications, the state-actionvaluefunction 4.35makesmoresensethanthestatevalue function 4.34. Because as shown in equation 4.43, the state-action value function directly defines how good or bad a control action is, andtheoptimalcontrollercanbesimplyconstructedusingthegreedy algorithm, which makes the controller design more straightforward than using the state value function. Due to this reason, all following RLmethodsareintroducedinthethestate-actionvaluefunctionform. Finally, as previously emphasized, the value function is the core of all RLCmethods. Usingdifferentapproachestocalculateorapproximate 116