First commit

sample.py

@@ -0,0 +1,68 @@
+import numpy as np
+import pandas as pd
+
+labels = list("ABCDEFGHIJKL")
+
+R = np.array([
+    [0,1,0,0,0,0,0,0,0,0,0,0], #A
+    [1,0,1,0,0,1,0,0,0,0,0,0], #B
+    [0,1,0,0,0,0,1,0,0,0,0,0], #C
+    [0,0,0,0,0,0,0,1,0,0,0,0], #D
+    [0,0,0,0,0,0,0,0,1,0,0,0], #E
+    [0,1,0,0,0,0,0,0,0,1,0,0], #F
+    [0,0,1,0,0,0,0,1,0,0,0,0], #G
+    [0,0,0,1,0,0,1,0,0,0,0,1], #H
+    [0,0,0,0,1,0,0,0,0,1,0,0], #I
+    [0,0,0,0,0,1,0,0,1,0,1,0], #J
+    [0,0,0,0,0,0,0,0,0,1,0,1], #K
+    [0,0,0,0,0,0,0,1,0,0,1,0], #L
+], dtype=float)
+
+# Fait une matrice de même dimension que R remplie de 0
+Q = np.zeros_like(R)
+
+print(type(R)) # recup le type
+print(R.ndim) # 2 -> matrice 2d
+print(R.shape) # (3, 3) -> 3 lignes 3 colonnes
+print(R.dtype) # float64
+print(R.size) # 9 éléments
+print(R.strides) # e.g. (24,8)
+
+# huperparamètre
+
+gamma = 0.75
+alpha = 0.90
+n_iters = 1000
+
+rng = np.random.default_rng(0)
+
+# Train Q-Learning for goal 'G'
+goal_label = 'G'
+goal = labels.index(goal_label)
+R_goal = R.copy()
+R_goal[goal, goal] = 1000.0
+
+for _ in range(n_iters):
+    s = rng.integers(0, R.shape[0]) # random current state
+    actions = np.where(R_goal[s] > 0)[0] # valid actions
+    if actions.size == 0:
+        continue
+    a = rng.choice(actions) # random valid action
+    s_next = a # transition to next state
+    # Calcul du time difference
+    TD = R_goal[s, a] + gamma * Q[s_next].max() - Q[s, a]
+    # Equation de Bellman
+    Q[s, a] += alpha * TD
+
+
+def best_path(start_label: str, goal_label: str):
+    s = labels.index(start_label)
+    g = labels.index(goal_label)
+    path = [start_label]
+    while s!= g:
+        s = np.argmax(Q[s]) # ici, on récupère l'action
+        path.append(labels[s])
+    return path
+
+
+print("Path E -> G: ", " -> ".join(best_path('E', 'G')))