Inteligencia artificial 11

1. Inteligência
Artificial
Nauber Gois
Aula 11

14. from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score

15. def buildClassifier():
clf=Sequential()
clf.add(Dense(output_dim=6,init='uniform',activation='relu',input_dim=10))
clf.add(Dense(output_dim=6,init='uniform',activation='relu'))
clf.add(Dense(output_dim=1,init='uniform',activation='sigmoid'))
clf.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
return clf

16. clf=KerasClassifier(build_fn=buildClassifier,batch_size=10,nb_epoch=100)
accuracy=cross_val_score(estimator=clf,X=X_train,y=y_train,cv=10,n_jobs=-1)

17. Reinforcement Learning

24. Exploration vs Exploitation
GridWorld

26. Markov Decision Process

32. import gym
env = gym.make('CartPole-v0')
env.reset()
for _ in range(1000):
env.render()
env.step(env.action_space.sample())

33. import gym
env = gym.make('CartPole-v0')
for i_episode in range(20):
observation = env.reset()
for t in range(100):
env.render()
print(observation)
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(t
+1))
break

35. import gym
env = gym.make('CartPole-v0')
print(env.action_space)
#> Discrete(2)
print(env.observation_space)
#> Box(4,)
The Discrete space allows a fixed range of non-negative numbers, so in
this case valid actions are either 0 or 1. The Box space represents an n-
dimensional box, so valid observations will be an array of 4 numbers. We
can also check the Box's bounds:

47. import gym
env = gym.make('AirRaid-ram-v0')
for i_episode in range(20):
observation = env.reset()
for t in range(100):
env.render()
print(observation)
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(t
+1))
break