lrp.b = 0.5
bestdepth = np.zeros(num_actions)
bestdepth1 = np.zeros(num_actions)
current_best = 0
current_best1 = 0
for j in range(n):
# reset the action probabilities.
# lrp.reset_actions()
count = 0
# lrp.b = tune.find_optimal_b(lrp, env, det_obj)
# Run a single experiment. Terminate if it reaches 10000 iterations.
while(True and count < 10000):
# Define m as the next action predicting the depth of the object.
m = lrp.next_action()
# Define req as the next detectable object depth.
req = det_obj.request()
# reward if m = req.
resp = env.response(m, req)
if(not resp):
lrp.do_reward(m)
else:
lrp.do_penalty(m)
if(max(lrp.p) > 0.999):
# The best depth counting from 0.
# Break at 98% convergence to a single depth.
bestdepth[np.argmax(lrp.p)] += 1
break
count += 1
if (current_best != np.argmax(bestdepth)):
receiver.goto(-100, depths[np.argmax(bestdepth)])
current_best = np.argmax(bestdepth)
import helpers as h
import math
num_actions = 6
env = Environment(num_actions)
dlri = DLRI(num_actions)
bestdepth = np.zeros(num_actions)
E = [0.1, 0.2, 0.4, 0.2, 0.01, 0.09]
det_obj = Pinger(E)
for k in range(5):
for j in range(1000):
# Caught me again...
dlri.p = np.array(h.make_dp(num_actions))
m = math.floor(num_actions / 2)
while (True):
req = det_obj.request()
resp = env.response(m, req)
if (not resp):
dlri.do_reward(m)
else:
dlri.do_penalty()
m = dlri.next_action()
if (max(dlri.p) == (num_actions * num_actions)):
# The best depth counting from 0 (seasurface).
bestdepth[np.argmax(dlri.p)] += 1
break
# print("The best depth tally is : " + str(bestdepth))
print("Converge on depth: " + str(np.argmax(bestdepth)))
print("The probability vector is: " + str(bestdepth / sum(bestdepth)))
a = tune.find_optimal_a(test_lrp, env, penalizer)
print("The value for a after tuning is " + str(test_lrp.a))
b = tune.find_optimal_b(test_lrp, env, penalizer)
print("The value for b after tuning is " + str(test_lrp.b))
test_lrp.a = a
test_lrp.b = b
n = 10000
bestdepth = np.zeros(5)
for j in range(n):
# reset the action probabilities.
test_lrp.reset_actions()
# Run a single experiment. Terminate if it reaches 10000 iterations.
while (True):
# Define m as the next action predicting the depth of the object.
m = test_lrp.next_action()
# Define req as the next detectable object depth.
req = penalizer.request()
# reward if m = req.
resp = env.response(m, req)
if (not resp):
test_lrp.do_reward(m)
else:
test_lrp.do_penalty(m)
if (max(test_lrp.p) > 0.98):
# The best depth counting from 0.
# Break at 98% convergence to a single depth.
bestdepth[np.argmax(test_lrp.p)] += 1
break
print("The desired probability vector is: " + str(penaly_probs))
print("The actual probability vector is: " + str(bestdepth / sum(bestdepth)))
