def test_agent(self, agent, input_height, input_width):
max_test_iter = 12
shortest_path = 4
j = 0
mp_lock.acquire()
state = self.last_state.get_last_state()
mp_lock.release()
paths = np.zeros((max_test_iter + 1, 1, 1, input_height, input_width), dtype='int32')
paths[j] = state
rewards = []
# Begin test phase
while True:
action = agent.choose_action(state, 0)
self.world.act(action)
sleep(0.2)
mp_lock.acquire()
state_prime = self.last_state.get_last_state()
mp_lock.release()
reward, terminal = self.calculate_reward_and_terminal(state_prime)
state = state_prime
j += 1
paths[j] = state
rewards.append(reward)
if j == max_test_iter and reward < 10:
print('not successful, no reward found after {} moves').format(max_test_iter)
break
elif terminal == 1:
print('path found.')
break
reward_total = 0
for i in range(j + 1):
print paths[i]
for num in rewards:
reward_total += num
print "Total Reward: {}".format(reward_total)
if j <= shortest_path + 1 and reward_total >= 10:
print('success!')
else:
print('fail :(')
# visualize the weights for each of the action nodes
weights = agent.get_weights()
plot_weights(weights)
print('Testing whether optimal path is learned ... '),
shortest_path = 5
state = s1
terminal = 0
path = np.zeros((5, 5))
path += state[0, 0, :, :]
i = 0
while terminal == 0:
action = agent.choose_action(state, 0)
state_prime, reward, terminal = world.act(state, action)
state = state_prime
path += state[0, 0, :, :]
i += 1
if i == 20 or reward == -1:
print('fail :(')
if np.sum(path) == shortest_path:
print('success!')
else:
print('fail :(')
print('Path: ')
print(path)
# visualize the weights for each of the action nodes
weights = agent.get_weights()
plot_weights(weights)
print('Testing whether optimal path is learned ... '),
shortest_path = 5
state = s1
terminal = 0
path = np.zeros((5, 5))
path += state[0, 0, :, :]
i = 0
while terminal == 0:
action = agent.choose_action(state, 0)
state_prime, reward, terminal = world.act(state, action)
state = state_prime
path += state[0, 0, :, :]
i += 1
if i == 20 or reward == -1:
print('fail :(')
if np.sum(path) == shortest_path:
print('success!')
else:
print('fail :(')
print('Path: ')
print(path)
# visualize the weights for each of the action nodes
weights = agent.get_weights()
plot_weights(weights)
