Seite - 125 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources

4.2. IntelligentControl creteusingeitherdiscretization[DKS+95]orfuzzydescriptions[LJ00] [Lin03], and then apply the lookup table based TD learning such as Q-learning and sarsa. After some preliminary tests and comparisons, the lookup table based Watkins’Q(λ)-learning [WD92] is selected as theTDlearningcontrolmethod. The aforementioned sarsa (equation 4.44) andQ-learning (equation 4.48) are the most basic one-step TD methods that only update the value function of the current state or state-action pair based on the next state or state-action pair. At each time only one state or state- action value is updated and the overall convergence speed is limited. In order to fully exploit the usefulness of each reward and speed up the entire learning process, a more efficient TD(λ) learning method is developed [Tes95] [WS98]. The parameter λ refers to the use of an eligibility trace [LS98],which isnormallydefinedas e(s,u) = { γλe(s,u)+1, ifs,u is thecurrentstate-actionpair, γλe(s,u), Otherwise, (4.50) where γ is the same as in equation 4.44 or 4.48 and 0 ≤ λ ≤ 1 is the fading factor. Intuitively, the eligibility trace is considered as the temporary memory of the occurrence of each state-action pair. Each time when there is a reward, not only the current state-action pair but also former implemented state-action pairs should be assigned credit. The relevance between each state-action pair and the current reward is adjusted using the fading factorλ, indicating the fact that the rele- vanceisdecayingexponentiallyandthecurrentstate-actionpair takes themaincredit. The involvement of the eligibility trace makes the TD(λ) method a combination of MC and pure TD (or TD(0)). When λ = 0, there is no former state-action pair recorded and then the method is equiv- alent to pure TD. When λ = 1, all former state-action pairs are recorded and the memory never fades, which means all state-action pairstakethesameweightofcredit fromthecurrentreward. Thenthe methodbecomesaonlineversionofMCmethod. Whentheeligibility trace is combined withQ-learning, there are mainly two different ap- proaches, the so-called Watkins’Q(λ) and Peng’sQ(λ) [PW96]. Here the Watkins’Q(λ) learning algorithm is selected because it is practi- 125