def run():
# build environment using openai gym
env = gym.make('MountainCar-v0')
env = env.unwrapped
sess = tf.Session()
# create an actor and critic
actor = Actor(sess, n_actions=n_actions, n_features=n_features, lr=lr_actor)
critic = Critic(sess, n_features=n_features, lr=lr_critic)
# build the two networks
actor.build_net()
critic.build_net()
sess.run(tf.global_variables_initializer())
# tf.summary.FileWriter("",sess.graph)
# count steps
step = 0
# env.render()
for episode in range(n_episodes):
s = env.reset()
# comment the render() to speed up
# env.render()
# s returned by gym is a vector, we need to transform it into a matrix
s = s[np.newaxis, :]
a = actor.choose_action(s)
while (True):
step += 1
# a new transition
s_, r, done, info = env.step(a)
# in order to let s_ add one rank(matrix)
s_ = s_[np.newaxis, :]
a_ = actor.choose_action(s_)
# calculate td_error
td_error = critic.learn(s, s_)
actor.learn(s, a, td_error)
s = s_
if step % 500 == 0:
print(step, s_)
if done:
print('arrive')
print(s_)
break
#position and velocity
n_features = 2
#0,1,2
n_actions = 3
n_episode = 1000
if __name__ == '__main__':
#build environment using openai gym
env = gym.make('MountainCar-v0')
env = env.unwrapped
sess = tf.Session()
#create an actor and critic
actor = Actor(sess,n_actions=n_actions,n_features=n_features,lr=lr_actor)
critic = Critic(sess,n_features=n_features,lr=lr_critic)
#build the two networks
actor.build_net()
critic.build_net()
sess.run(tf.global_variables_initializer())
#tf.summary.FileWriter("",sess.graph)
#count steps
step = 0
#env.render()
for episode in range(n_episode):
s = env.reset()
#comment the render() to speed up
#env.render()
#s returned by gym is a vector, we need to transform it into a matrix
s = s[np.newaxis, :]
a = actor.choose_action(s)
