本論文提出四個新的且不同的學習演算法，分別是逆向Q學習法、模糊Q學習結合逆向Q學習法 (FQLBQ)、混合基因演算法與模糊Sarsa學習法 (HGAFSL)以及類演化程序的粒子群最佳化演算法 (EPSO)。首先，我們集中在如何結合Q學習以及Sarsa演算法，並提出一種新的方法叫做逆向Q學習，並且可以與Sarsa演算法或者其它的加強式學習演算法結合。逆向Q學習演算法是直接地調整Q值，然後間接地影響動作選擇的策略。其次，逆向Q學習被利用來結合模糊Q學習，模糊Q學習是使用來調整與學習模糊控制系統的後件步，逆向Q學習是被利用來加快模糊Q學習的速度。第三，為了克服傳統的基因演算法隨機地挑選交配點，我們提出了HGAFSL來快速地調整模糊規則的後件步，當每個個體計算其適應值時，模糊Sarsa學習方法將會同時地計算每一個基因的Q值，Q值會被當作一種預測的資訊，這些資訊會被用來判斷個體或群體的基因是好或是壞。因此，根據Q值並且利用輪盤選擇法選擇多點交配且多對雙親去執行交配，以取代隨機交配的方法。
最後，用EPSO來快速學習顏色資訊的設置，並且執行在機械手臂的控制系統。機械手臂必須抓取有顏色的物體，並且放置到正確的位置裡，其顏色辨識是經由RGB或HSV的色彩模型，一般的方法是經由手動調整其閥值，但是卻非常的耗時以至於無法得到較好的閥值來分割彩色影像。所以，我們提出半自動的學習方法來學習顏色的資訊，先利用分水嶺演算法與使用者互動以分割彩色影像並得到目標影像，然後再比較目標影像與原始影像以建立顏色資訊的查表法，再利用提出的EPSO方法來學習HSV的三個閥值，由實驗結果得知，EPSO可以較快速地學習到閥值以分割彩色影像，且能夠跳出局部最小值。

In this dissertation, we propose four different novel algorithms including backward Q-learning, fuzzy Q-learning based on backward Q-learning (FQLBQ), a hybrid of genetic algorithm and fuzzy Sarsa learning (HGAFSL), and evolutionary programming like particle swarm optimization (EPSO), respectively. First, we focus on how to combine Q-learning with the Sarsa algorithm, and presents a new method, called backward Q-learning, which can be implemented with the Sarsa algorithm or other reinforcement learning (RL) algorithm. The backward Q-learning directly tunes the Q-values, and then the Q-values will indirectly affect the action selection policy. Second, the backward Q-learning is utilized to integrate with the fuzzy Q-learning (FQL). The FQL is applied to tune and learn the consequence part of the fuzzy control system, and the backward Q-learning is employed to enhance learning speed of FQL. Third, we offer HGAFSL to fast tune the consequent part of the fuzzy rules in order to overcome the conventional GA randomly chooses the crossover point. When each individual estimates the fitness, the FSL will simultaneously compute the Q-value of every gene. The Q-value is regarded as the predicted information that can assist the GA in distinguishing the better or worse gene from an individual or population. Hence, the crossover operation will select multiple crossover point and multiple parents by roulette wheel selection (RWS) according to the Q-value instead of random choice.
Finally, a fast color information setup based on EPSO for the manipulator control system is examined. The first step for a manipulator to grasp and place color objects into the correct location is to correctly identify the RGB or the corresponding HSV (Hue, Saturation, Value) color model. The commonly used method to determine the thresholds of HSV range is manual tuning, but it is time-consuming to find the best boundary to segment the color image. Therefore, we propose a new method to learn color information, which is executed by semi-automatic learning. The watershed algorithm incorporates user interactions to segment the color image and obtain the target image. Then, the comparison between the target image and the original image is utilized to build a lookup table (LUT) of color information, where three HSV thresholds are learned by EPSO methods. The EPSO methods can not only rapidly learn the thresholds to segment a color image but can also jump out the local minimum.

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