Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents
Arthur Juliani
1.3K47

Hi Arthur,

I’m trying to implement your approach for 2 neurons as out put. Each neuron for each action. So now I directly use the probability of the action we take(0 or 1) from the network to compute the loss function.

But it doesn’t really work. the problem could be here:

loglik = tf.log(probability[0,input_y[0,0]])

Would you please check it?I use python 3.5.

Thank you.

import numpy as np
import pickle as pickle
import tensorflow as tf
#%matplotlib inline
import matplotlib.pyplot as plt
import math

import gym
env = gym.make('CartPole-v0')

env.reset()
random_episodes = 0
reward_sum = 0
while random_episodes < 1:
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 = 10 # number of hidden layer neurons
batch_size = 50 # every how many episodes to do a param update?
learning_rate = 1e-2 # 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 to
#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, 2],
initializer=tf.contrib.layers.xavier_initializer())
score = tf.matmul(layer1,W2)
probability = tf.nn.softmax(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.int32,[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))
loglik = tf.log(probability[0,input_y[0,0]])
loss = -tf.reduce_sum(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(0, r.size)):
running_add = running_add * gamma + r[t]
discounted_r[t] = running_add
return discounted_r


xs, hs, dlogps, drs, ys, tfps = [], [], [], [], [], []
running_reward = None
reward_sum = 0
episode_number = 1
total_episodes = 10000
init = tf.initialize_all_variables()

# 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

prob = np.reshape(tfprob, 2)
action = np.random.choice(a=[0, 1], p=prob)

xs.append(x) # observation
# y = 1 if action == 0 else 0 # a "fake label"

ys.append(action)

# 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)
tfp = tfps
xs, hs, dlogps, drs, ys, tfps = [], [], [], [], [], [] # 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 %f. Total average reward %f.' % (reward_sum / batch_size, running_reward / batch_size))

if reward_sum / batch_size > 200:
print("Task solved in", episode_number, 'episodes!')
break

reward_sum = 0

observation = env.reset()

print(episode_number, 'Episodes completed.')