def choose_action(step):
image = get_observation()
history = np.array([image]*4)
history_batch = np.array([history])
prediction = model.predict(history_batch)[0]
best_action = legal_actions[np.argmax(prediction)]
random_action = random.choice(legal_actions)
EPSILON = epsilon(step)
action = select_with_probability([random_action, best_action], [EPSILON, 1-EPSILON])
print EPSILON, action
return best_action
def choose_action(step):
image = get_observation()
history = np.array([image] * 4)
history_batch = np.array([history])
prediction = model.predict(history_batch)[0]
best_action = legal_actions[np.argmax(prediction)]
random_action = random.choice(legal_actions)
EPSILON = epsilon(step)
action = select_with_probability([random_action, best_action],
[EPSILON, 1 - EPSILON])
print EPSILON, action
return best_action
def choose_action(image, step, epoch):
history = np.array([image]*4)
history_batch = np.array([history])
prediction = model.predict(history_batch)[0]
best_action = legal_actions[np.argmax(prediction)]
random_action = random.choice(legal_actions)
#EPSILON = 1.0
if MODE == "test":
EPSILON = 0.0
elif MODE == "random":
EPSILON = 1.0
else:
EPSILON = epsilon(step, epoch)
action = select_with_probability([random_action, best_action], [EPSILON, 1-EPSILON])
print "Step: %d, Epsilon: %f, Epoch: %d" % (step, EPSILON, epoch)
return best_action
def choose_action(image, step, epoch):
history = np.array([image] * 4)
history_batch = np.array([history])
prediction = model.predict(history_batch)[0]
best_action = legal_actions[np.argmax(prediction)]
random_action = random.choice(legal_actions)
#EPSILON = 1.0
if MODE == "test":
EPSILON = 0.0
elif MODE == "random":
EPSILON = 1.0
else:
EPSILON = epsilon(step, epoch)
action = select_with_probability([random_action, best_action],
[EPSILON, 1 - EPSILON])
print "Step: %d, Epsilon: %f, Epoch: %d" % (step, EPSILON, epoch)
return best_action
