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Qlearning
1.
Introduction to Q-Learning 20160505 Yuhsuan
Chen
2.
2 Q - Learning 增強式學習法(Reinforcement
learning) 是根源自心理學大師帕弗洛夫(Pavlov)的制約實驗,這個實驗讓狗把鈴聲和食物 聯想在一起。每次餵食前都先讓狗聽一段鈴聲,久而久之,只要鈴聲響起,即使食 物沒有出現,狗也會因預期心裡而流涎三尺,這就是有名的帕弗洛夫的制約理論 (Pavlovian conditioning theory) Q-Learning演算法是加強式學習中的其中一種。增強式學習是模仿動物的學習過程 ,其學習的經驗來自與環境間的互動,透過學習領悟在各種狀態下選擇哪種策略行 動,以達成目標及任務
3.
3 Q - Learning •
學習代理人(Agent) • 學習者的策略動作(Action) • 與學習者互動的環境(Environment) • 給予學習者的策略動作好壞的回饋值(Reward) • 學習者的狀態(State)
4.
4 Q - Learning 系統利用Q值來累積每次行動後所學到的經驗,獲得Q值為一個預期報酬,經過多 次學習之後,才會達到一個理想值Q*,而Q*會選擇以下一個狀態Q預期報酬最高代 替 •
Q(s,a)表示在狀態s下採取動作a的預期報酬 • s為狀態向量 • a為動作向量 • R為根據動作向量a所得到回饋值 • γ為折扣率,介於0到1之間,意義上是所學到經驗會隨著時間流逝、淡忘,在數 學上的意義是讓回饋值r在經過多次累加之後,不至於發散 • α為學習率,介於0到1之間。通常在系統剛開始學習時,會將α設為1,表示對 剛開始學習的權重給予1。經過不斷學習,α逐漸降為0,也就是將不再學習。
5.
5 Q - Learning 假設我們有五間房間並編號為0~4,1跟4號可以直接通到屋外(5),如下圖: 如果我們的目的是要從各房間走到戶外,路徑圖可標示為下圖:
6.
6 Q - Learning 我們的代理人(Agent)可能位於任何一個位置(0~5),目標是走到屋外(5) 並且設定每一個路徑的獎賞,如下圖: 根據路徑圖,我們可以描繪出reward陣列(R),左邊代表起始號碼,右邊代表移動 的號碼,中間為獎賞。 •
代理人起始位置為5,可直接獲得獎賞100 • 代理人起始位置1或4時,走到5,就可以獲得獎賞100 • 如果沒有抵達目標,獎賞則是0 • 無法抵達的路徑則為-1
7.
7 Q - Learning 假設我們的學習率先設定為0.8,起始房間為1,初始陣列Q為下 起始為1,有3跟5可以選擇,假設我們選擇5 在5的時候有三種路徑可以選,1、4、5,此時套用公式將我們的陣列Q更新 •
Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)] • Q(1, 5) = R(1, 5) + 0.8 * Max[Q(5, 1), Q(5, 4), Q(5, 5)] = 100 + 0.8 * 0 = 100
8.
8 Q - Learning 接著以3為我們的起始狀態 起始為3,有145可以選擇,假設我們選擇1 在1的時候有兩種路徑可以選,3、5,此時套用公式將我們的陣列Q更新 •
Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)] • Q(3, 1) = R(3, 1) + 0.8 * Max[Q(1, 3), Q(1, 5)] = 0 + 0.8 * Max(0, 100) = 80
9.
9 Q - Learning 以剛剛的方式讓他自動執行後,可以獲得最佳路徑 假設起始位置為2,則可以得到最佳路徑
2→3→1→5
10.
10 Q - Learning 以Flappy
Bird為例 S包含: Flappy Bird到水管的水平距離 Flappy Bird到水管的垂直距離 Flappy Bird到天空的距離 A包含: 跳躍、不跳躍 R包含: Flappy Bird活著+1 Flappy Bird死亡-1000 水管水平距離 天空距離 水管垂直距離
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