Initial import

git-svn-id: http://moon:8086/svn/projects/RL-lab@339 fda53097-d464-4ada-af97-ba876c37ca34

rl_lab.py

@@ -0,0 +1,89 @@
+import numpy as np
+
+
+lab_size_x = 5
+lab_size_y = 5
+num_doors_per_room = 4
+
+
+P_door = np.ndarray((lab_size_x, lab_size_y, num_doors_per_room))
+P_door = np.random.uniform(0,1,P_door.shape)
+
+print('P_door.shape = ', P_door.shape)
+lab = np.rint(np.random.uniform(0, num_doors_per_room-1, size=(lab_size_x, lab_size_y)))
+
+
+#    1
+#    |
+# 4 -0- 2
+#    |
+#    3
+
+move = np.array([(0,0), (-1,0), (0,1), (1,0), (0,-1)])
+lab = np.array([
+	[3, 0, 0, 0, -1],
+	[3, 0, 0, 2, 1],
+	[2, 2, 3, 1, 4],
+	[3, 4, 4, 2, 1],
+	[2, 2, 2, 1, 0]
+])
+
+def choose_door(Pn):
+	Pnorm = np.cumsum(Pn)
+	size = len(Pn)
+	Z = np.random.uniform(0,1)
+
+	result = None
+	for n in range(0, size):
+		if Z <= Pnorm[n]:
+			result = n
+			break
+
+	return result
+
+def run(pos, N_trials, learning_rate):
+	lab_visited = np.zeros(P_door.shape)
+	moves_needed = 0
+	for n in range(0, N_trials):
+
+#		print ("Pos={}".format(pos))
+		if lab[pos] == -1:
+			break
+
+		P = P_door[pos]
+		Pn = P/np.sum(P)
+
+		while True:
+			door = choose_door(Pn)
+			pos_new = tuple(pos + move[door + 1])
+
+			if pos_new[0] < 0 or pos_new[1] < 0:
+				continue
+
+			if pos_new[0] >= lab_size_x or pos_new[1] >= lab_size_y:
+				continue
+
+			if lab[pos_new] == 0:
+				continue
+
+			break
+
+		moves_needed += 1
+		k = lab_visited[pos][door]
+		P[door] = max(0, P[door] - learning_rate)
+		P_door[pos] = P
+
+		pos = pos_new
+		lab_visited[pos][door] += 1
+
+	return moves_needed, lab_visited
+
+for i in range(0, 100):
+	moves_needed, lab_visited = run(pos=(0,0), N_trials=1000, learning_rate=0.001)
+	print ('Visited map after {} trials: {}'.format(moves_needed, lab_visited))
+	print ('P_door after {} trials: {}'.format(moves_needed, P_door))
+
+moves_needed, lab_visited = run(pos=(0,0), N_trials=1000, learning_rate=0.0)
+print ('Finished after {} trials'.format(moves_needed))
+
+#	P_door[pos, door] += learning_rate