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Import numpy as np
Import tensorflow as tf
Import gym
Env = gym. make ('cartpole-v0 ')
Env. reset ()
Random_episodes = 0
Reward_sum = 0
While random_episodes <10:
Env. render ()
Observation, reward, done, _ = env. step (np. random. randint (0, 2 ))
Reward_sum + = reward
If done:
Random_episodes + = 1
Print ("Reward for this episode was:", reward_sum)
Reward_sum = 0
Env. reset ()

# Hyperparameters
H = 50 # number of hidden layer neurons
Batch_size = 25 # every how many episodes to do a param update?
Learning_rate = 1e-1 # feel free to play with this to train faster or more stably.
Gamma = 0.99 # discount factor for reward
D = 4 # input dimensionality
Tf. reset_default_graph ()
# This defines the network as it goes from taking an observation of the environment
# Giving a probability of chosing to the action of moving left or right.
Observations = tf. placeholder (tf. float32, [None, D], name = "input_x ")
W1 = tf. get_variable ("W1", shape = [D, H],
Initializer = tf. contrib. layers. xavier_initializer ())
Layer1 = tf. nn. relu (tf. matmul (observations, W1 ))
W2 = tf. get_variable ("W2", shape = [H, 1],
Initializer = tf. contrib. layers. xavier_initializer ())
Score = tf. matmul (layer1, W2)
Probability = tf. nn. sigmoid (score)
# From here we define the parts of the network needed for learning a good policy.
Tvars = tf. trainable_variables ()
Input_y = tf. placeholder (tf. float32, [None, 1], name = "input_y ")
Advantages = tf. placeholder (tf. float32, name = "reward_signal ")
# The loss function. This sends the weights in the direction of making actions
# That gave good advantage (reward over time) more likely, and actions that didn't less likely.
Loglik = tf. log (input_y * (input_y-probability) + (1-input_y) * (input_y + probability ))
Loss =-tf. performance_mean (loglik * advantages)
NewGrads = tf. gradients (loss, tvars)
# Once we have collected a series of gradients from multiple episodes, we apply them.
# We don't just apply gradeients after every episode in order to account for noise in the reward signal.
Adam = tf. train. AdamOptimizer (learning_rate = learning_rate) # Our optimizer
W1Grad = tf. placeholder (tf. float32, name = "batch_grad1") # Placeholders to send the final gradients through when we update.
W2Grad = tf. placeholder (tf. float32, name = "batch_grad2 ")
BatchGrad = [W1Grad, W2Grad]
UpdateGrads = adam. apply_gradients (zip (batchGrad, tvars ))
Def discount_rewards (r ):
"Take 1D float array of rewards and compute discounted reward """
Discounted_r = np. zeros_like (r)
Running_add = 0
For t in reversed (range (r. size )):
Running_add = running_add * gamma + r [t]
Discounted_r [t] = running_add
Return discounted_r

Xs, ys, drs = [], [], []
# Running_reward = None
Reward_sum = 0
Episode_number = 1
Total_episodes = 10000
Init = tf. global_variables_initializer ()
# Launch the graph
With tf. Session () as sess:
Rendering = False
Sess. run (init)
Observation = env. reset () # Obtain an initial observation of the environment
# Reset the gradient placeholder. We will collect gradients in
# GradBuffer until we are ready to update our policy network.
GradBuffer = sess. run (tvars)
For ix, grad in enumerate (gradBuffer ):
GradBuffer [ix] = grad * 0

While episode_number <= total_episodes:

# Rendering the environment slows things down,
# So let's only look at it once our agent is doing a good job.
If reward_sum/batch_size> 100 or rendering = True:
Env. render ()
Rendering = True

# Make sure the observation is in a shape the network can handle.
X = np. reshape (observation, [1, D])

# Run the policy network and get an action to take.
Tfprob = sess. run (probability, feed_dict = {observations: x })
Action = 1 if np. random. uniform () <tfprob else 0

Xs. append (x) # observation
Y = 1 if action = 0 else 0 # a "fake label"
Ys. append (y)
# Step the environment and get new measurements
Observation, reward, done, info = env. step (action)
Reward_sum + = reward
Drs. append (reward) # record reward (has to be done after we call step () to get reward for previous action)
If done:
Episode_number + = 1
# Stack together all inputs, hidden states, action gradients, and rewards for this episode
Epx = np. vstack (xs)
Epy = np. vstack (ys)
Epr = np. vstack (drs)
Xs, ys, drs = [], [], [] # reset array memory
# Compute the discounted reward backwards through time
Discounted_epr = discount_rewards (epr)
# Size the rewards to be unit normal (helps control the gradient estimator variance)
Discounted_epr-= np. mean (discounted_epr)
Discounted_epr/= np. std (discounted_epr)

# Get the gradient for this episode, and save it in the gradBuffer
TGrad = sess. run (newGrads, feed_dict = {observations: epx, input_y: epy, advantages: discounted_epr })
For ix, grad in enumerate (tGrad ):
GradBuffer [ix] + = grad

# If we have completed enough episodes, then update the policy network with our gradients.
If episode_number % batch_size = 0:
Sess. run (updateGrads, feed_dict = {W1Grad: gradBuffer [0], W2Grad: gradBuffer [1]})
For ix, grad in enumerate (gradBuffer ):
GradBuffer [ix] = grad * 0

# Give a summary of how well our network is doing for each batch of episodes.
# Running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
Print ('average reward for episode % d: % f. '% (episode_number, reward_sum/batch_size ))

If reward_sum/batch_size> 200:
Print ("Task solved in", episode_number, 'episodes! ')
Break

Reward_sum = 0

Observation = env. reset ()


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