jens
7d06ea0d23
git-svn-id: http://moon:8086/svn/projects/RL-lab@342 fda53097-d464-4ada-af97-ba876c37ca34
132 lines
2.6 KiB
Python
132 lines
2.6 KiB
Python
import numpy as np
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lab_size_x = 5
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lab_size_y = 5
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num_doors_per_room = 4
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P_door = np.ndarray((lab_size_x, lab_size_y, num_doors_per_room))
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P_door = np.ones(P_door.shape)
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print('P_door.shape = ', P_door.shape)
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lab = np.rint(np.random.uniform(0, num_doors_per_room-1, size=(lab_size_x, lab_size_y)))
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# 2
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# |
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# 3 -0- 1
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# |
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# 4
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move = np.array([(0,0), (0,1), (1,0), (0,-1), (-1,0)])
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lab = np.array([
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[4, 0, 0, 0, -1],
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[4, 0, 0, 0, 2],
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[4, 0, 1, 1, 2],
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[4, 0, 2, 0, 0],
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[1, 1, 2, 0, 0]
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])
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def to_this_door(door_last_room):
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q, r = divmod(door_last_room + 2, 4)
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return r
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def choose_door(Pn):
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Pnorm = np.cumsum(Pn)
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size = len(Pn)
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Z = np.random.uniform(0,1)
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result = None
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for n in range(0, size):
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if Z <= Pnorm[n]:
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result = n
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break
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return result
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def normalize(P):
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for x in range(0, lab_size_x):
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for y in range(0, lab_size_y):
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pos = (x,y)
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P0 = P[pos]
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P[pos] = P0/np.sum(P0)
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return P
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def forget(forgetting_factor):
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for x in range(0, lab_size_x):
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for y in range(0, lab_size_y):
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pos = (x,y)
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P_door[pos,:] *= forgetting_factor
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def run(pos, N_trials, learning_rate, penalty_factor=0.99, forgetting_factor=0.999):
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lab_visited = np.zeros((lab_size_x, lab_size_y))
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moves_needed = 0
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door_last = -1
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for n in range(0, N_trials):
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if lab[pos] == -1:
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break
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# forget(forgetting_factor)
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P = P_door[pos]
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Pn = P/np.sum(P)
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while True:
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fail = False
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door = choose_door(Pn)
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if door_last >= 0:
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this_door = to_this_door(door_last)
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if this_door == door:
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P_door[pos][door] *= (1.0 - learning_rate)
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pos_new = tuple(pos + move[door + 1])
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if pos_new[0] < 0 or pos_new[1] < 0:
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fail = True
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if pos_new[0] >= lab_size_x or pos_new[1] >= lab_size_y:
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fail = True
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try:
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if lab[pos_new] == 0:
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fail = True
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except:
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pass
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if fail:
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P_door[pos][door] *= penalty_factor
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continue
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else:
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P[door] = P[door] + learning_rate
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P_door[pos] = P
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break
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pos = pos_new
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lab_visited[pos] += 1
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moves_needed += 1
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door_last = door
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return moves_needed, lab_visited
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for i in range(0, 1000):
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moves_needed, lab_visited = run(pos=(0,0), N_trials=1000, learning_rate=0.5)
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print ('Visited map after {} moves:'.format(moves_needed))
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# print (lab_visited)
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# print ('P_door after {} moves:'.format(moves_needed))
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# print (P_door)
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moves_needed, lab_visited = run(pos=(0,0), N_trials=1000, learning_rate=0.0)
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print ('Finished after {} moves'.format(moves_needed))
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print('Visited map after {} moves:'.format(moves_needed))
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print(lab_visited)
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print ('P_door after {} moves:'.format(moves_needed))
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P = normalize(P_door)
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for i in range(0, num_doors_per_room):
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print (P[:,:,i])
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