def playActor(self):
self.load("NetworkParam/FinalParam")
hdg0_rand_vec=[0,7,12]
'''
WIND CONDITIONS
'''
mean = 45 * TORAD
std = 0.1 * TORAD
wind_samples = 10
w = wind(mean=mean, std=std, samples = wind_samples)
try:
for i in range(len(hdg0_rand_vec)):
# Initial state
WH = w.generateWind()
hdg0_rand = hdg0_rand_vec[i]
hdg0 = hdg0_rand * TORAD * np.ones(10)
s = self.env.reset(hdg0,WH)
episode_reward = 0
episode_step=0
v_episode=[]
i_episode=[]
while episode_step < 40: #not done:
if episode_step==0:
i_episode.append(hdg0_rand+WH[0]/TORAD-40)
else:
i_episode.append(s[0][-1]/TORAD)
s = np.reshape([s[0,:], s[1,:]], [self.state_size,1])
a, = self.sess.run(self.network.actions,
feed_dict={self.network.state_ph: s[None]})
a = np.clip(a, self.low_bound, self.high_bound)
s_, r = self.env.act(a,WH)
episode_reward += r
v_episode.append(r)
episode_step += 1
s = s_
DISPLAYER.displayVI(v_episode,i_episode,i)
print("Episode reward :", episode_reward," for incidence: ",hdg0_rand)
except KeyboardInterrupt as e:
pass
except Exception as e:
print("Exception :", e)
finally:
print("End of the demo")
def play(self, sess, number_run, path=''):
print("Playing", self.name, "for", number_run, "runs")
with sess.as_default(), sess.graph.as_default():
hdg0_rand_vec = [0, 7, 13]
'''
WIND CONDITIONS
'''
mean = 45 * TORAD
std = 0 * TORAD
wind_samples = 10
w = wind(mean=mean, std=std, samples=wind_samples)
try:
for i in range(number_run):
# Reset the local network to the global
if self.name != 'global':
sess.run(self.update_local_vars)
WH = w.generateWind()
hdg0_rand = hdg0_rand_vec[i]
hdg0 = hdg0_rand * TORAD * np.ones(10)
s = self.env.reset(hdg0, WH)
episode_reward = 0
episode_step = 0
v_episode = []
i_episode = []
done = False
#self.lstm_state = self.network.lstm_state_init
while (not done and episode_step < 70):
i_episode.append(round(s[0][-1] / TORAD))
s = np.reshape([s[0, :], s[1, :]],
[2 * self.state_size, 1])
# Prediction of the policy
feed_dict = {self.network.inputs: [s]}
policy, value = sess.run(
[self.network.policy, self.network.value],
feed_dict=feed_dict)
policy = policy[0]
# Choose an action according to the policy
action = np.random.choice([1.5, 0, -1.5], p=policy)
s_, r = self.env.act(action, WH)
if episode_step > 12:
if np.mean(v_episode[-4:]) > 0.8:
#done=True
print("Done!")
else:
done = False
episode_reward += r
v_episode.append(r)
episode_step += 1
s = s_
DISPLAYER.displayVI(v_episode, i_episode, i)
print("Episode reward :", episode_reward)
except KeyboardInterrupt as e:
pass
finally:
print("End of the demo")
def playCritic(self):
self.load("NetworkParam/FinalParam")
hdg0_rand_vec=[0,7,12]
'''
WIND CONDITIONS
'''
mean = 45 * TORAD
std = 0.1 * TORAD
wind_samples = 10
w = wind(mean=mean, std=std, samples = wind_samples)
try:
for i in range(len(hdg0_rand_vec)):
# Initial state
WH = w.generateWind()
hdg0_rand = hdg0_rand_vec[i]
hdg0 = hdg0_rand * TORAD * np.ones(10)
s = self.env.reset(hdg0,WH)
episode_reward = 0
episode_step=0
v_episode=[]
i_episode=[]
while episode_step < 30: #not done:
if episode_step==0:
i_episode.append(hdg0_rand+WH[0]/TORAD-40)
else:
i_episode.append(s[0][-1]/TORAD)
# Critic policy
critic = [self.evaluate(s, -1.5),self.evaluate(s, -1.25),self.evaluate(s, -1),
self.evaluate(s, -0.75),self.evaluate(s, -0.5),self.evaluate(s, -0.25),self.evaluate(s, 0),self.evaluate(s, 0.25),
self.evaluate(s, 0.5),self.evaluate(s, 0.75),self.evaluate(s, 1),self.evaluate(s, 1.25),
self.evaluate(s, 1.5)]
a = np.argmax(critic)
if a == 0:
a = -1.5
if a == 1:
a = -1.25
if a == 2:
a = -1
if a == 3:
a = -0.75
if a == 4:
a = -0.5
if a == 5:
a = -0.25
if a == 6:
a = 0
if a == 7:
a = 0.25
if a == 8:
a = 0.5
if a == 9:
a = 0.75
if a == 10:
a = 1
if a == 11:
a = 1.25
if a == 12:
a = 1.5
s_, r = self.env.act(a,WH)
episode_reward += r
v_episode.append(r)
episode_step += 1
s = s_
DISPLAYER.displayVI(v_episode,i_episode,i+3)
print("Episode reward :", episode_reward," for incidence: ",hdg0_rand)
except KeyboardInterrupt as e:
pass
except Exception as e:
print("Exception :", e)
finally:
print("End of the demo")