blog:2019:0310_dqn

# Introduction to Deep Q-Networks

Now finally something that sounds a bit more challenging on my Reinforcement Learning journey: Deep Q-Networks! Even if, when you start analysis the structure of those networks, they are still pretty simple in the end. But still, there are 3 key elements that we should consider here: convolutional layers, experience replay, and Target network usage.

On top of that we will also initroduce the Double DQN and Dueling DQN extensions, that are used to improve the system stability and performances. Let's get started!

• Experience Replay: Experience replay consists in storing complete experience rollouts from the agent and then select a batch of those experiences randomly, to reapply them in the agent training. This helps improving the agent performances and prevent it from only learning on the most recent experience available from the environment. When there is
• Double DQN: This mainly consist in using our primary network to select an action, and then using our “target network” to generate the target Q-Value.
• So our reference implementation is here:
import numpy as np
import random
import gym

import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np

from nv.core.utils import *
from nv.deep_learning.gridworld import gameEnv

# cf. https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df

# Implementing the network itself:
class Qnetwork():
def __init__(self,h_size, env):
#The network recieves a frame from the game, flattened into an array.
#It then resizes it and processes it through four convolutional layers.
self.scalarInput =  tf.placeholder(shape=[None,21168],dtype=tf.float32)
self.imageIn = tf.reshape(self.scalarInput,shape=[-1,84,84,3])
self.conv1 = slim.conv2d( \
self.conv2 = slim.conv2d( \
self.conv3 = slim.conv2d( \
self.conv4 = slim.conv2d( \

#We take the output from the final convolutional layer and split it into separate advantage and value streams.
self.streamAC,self.streamVC = tf.split(self.conv4,2,3)
self.streamA = slim.flatten(self.streamAC)
self.streamV = slim.flatten(self.streamVC)
xavier_init = tf.contrib.layers.xavier_initializer()
self.AW = tf.Variable(xavier_init([h_size//2,env.actions]))
self.VW = tf.Variable(xavier_init([h_size//2,1]))
self.Value = tf.matmul(self.streamV,self.VW)

#Then combine them together to get our final Q-values.
self.predict = tf.argmax(self.Qout,1)

#Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
self.targetQ = tf.placeholder(shape=[None],dtype=tf.float32)
self.actions = tf.placeholder(shape=[None],dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions,env.actions,dtype=tf.float32)

self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis=1)

self.td_error = tf.square(self.targetQ - self.Q)
self.loss = tf.reduce_mean(self.td_error)
self.updateModel = self.trainer.minimize(self.loss)

# Experience replay storage class:
class experience_buffer():
def __init__(self, buffer_size = 50000):
self.buffer = []
self.buffer_size = buffer_size

if len(self.buffer) + len(experience) >= self.buffer_size:
self.buffer[0:(len(experience)+len(self.buffer))-self.buffer_size] = []
self.buffer.extend(experience)

def sample(self,size):
return np.reshape(np.array(random.sample(self.buffer,size)),[size,5])

# Function to resize our game frames.
def processState(states):
return np.reshape(states,[21168])

# These functions allow us to update the parameters of our target network with those of the primary network.
def updateTargetGraph(tfVars,tau):
total_vars = len(tfVars)
op_holder = []
for idx,var in enumerate(tfVars[0:total_vars//2]):
op_holder.append(tfVars[idx+total_vars//2].assign((var.value()*tau) + ((1-tau)*tfVars[idx+total_vars//2].value())))
return op_holder

def updateTarget(op_holder,sess):
for op in op_holder:
sess.run(op)

def train_deep_q_network(path):
logDEBUG("Building environment...")

env = gameEnv(partial=False,size=5)

# training parameters:
batch_size = 32 #How many experiences to use for each training step.
update_freq = 4 #How often to perform a training step.
y = .99 #Discount factor on the target Q-values
startE = 1 #Starting chance of random action
endE = 0.1 #Final chance of random action
annealing_steps = 10000. #How many steps of training to reduce startE to endE.
num_episodes = 10000 #How many episodes of game environment to train network with.
pre_train_steps = 10000 #How many steps of random actions before training begins.
max_epLength = 50 #The max allowed length of our episode.
h_size = 512 #The size of the final convolutional layer before splitting it into Advantage and Value streams.
tau = 0.001 #Rate to update target network toward primary network

# Actual training process:
tf.reset_default_graph()
mainQN = Qnetwork(h_size, env)
targetQN = Qnetwork(h_size, env)

init = tf.global_variables_initializer()

saver = tf.train.Saver()

trainables = tf.trainable_variables()

targetOps = updateTargetGraph(trainables,tau)

myBuffer = experience_buffer()

#Set the rate of random action decrease.
e = startE
stepDrop = (startE - endE)/annealing_steps

#create lists to contain total rewards and steps per episode
jList = []
rList = []
total_steps = 0

# ensure the provided path already exists:
CHECK(nvDirExists(path), "Invalid storage path: %s" % path)

with tf.Session() as sess:
sess.run(init)
ckpt = tf.train.get_checkpoint_state(path)
saver.restore(sess,ckpt.model_checkpoint_path)
for i in range(num_episodes):
episodeBuffer = experience_buffer()
#Reset environment and get first new observation
s = env.reset()
s = processState(s)
d = False
rAll = 0
j = 0
#The Q-Network
while j < max_epLength: #If the agent takes longer than 200 moves to reach either of the blocks, end the trial.
j+=1
#Choose an action by greedily (with e chance of random action) from the Q-network
if np.random.rand(1) < e or total_steps < pre_train_steps:
a = np.random.randint(0,4)
else:
a = sess.run(mainQN.predict,feed_dict={mainQN.scalarInput:[s]})[0]
s1,r,d = env.step(a)
s1 = processState(s1)
total_steps += 1
episodeBuffer.add(np.reshape(np.array([s,a,r,s1,d]),[1,5])) #Save the experience to our episode buffer.

if total_steps > pre_train_steps:
if e > endE:
e -= stepDrop

if total_steps % (update_freq) == 0:
trainBatch = myBuffer.sample(batch_size) #Get a random batch of experiences.
#Below we perform the Double-DQN update to the target Q-values
Q1 = sess.run(mainQN.predict,feed_dict={mainQN.scalarInput:np.vstack(trainBatch[:,3])})
Q2 = sess.run(targetQN.Qout,feed_dict={targetQN.scalarInput:np.vstack(trainBatch[:,3])})
end_multiplier = -(trainBatch[:,4] - 1)
doubleQ = Q2[range(batch_size),Q1]
targetQ = trainBatch[:,2] + (y*doubleQ * end_multiplier)
#Update the network with our target values.
_ = sess.run(mainQN.updateModel, \
feed_dict={mainQN.scalarInput:np.vstack(trainBatch[:,0]),mainQN.targetQ:targetQ, mainQN.actions:trainBatch[:,1]})

updateTarget(targetOps,sess) #Update the target network toward the primary network.
rAll += r
s = s1

if d == True:

break

jList.append(j)
rList.append(rAll)
#Periodically save the model.
if i % 1000 == 0:
saver.save(sess,path+'/model-'+str(i)+'.ckpt')
logDEBUG("Saved Model")
if len(rList) % 10 == 0:
logDEBUG("Total steps: %d, mean value: %f, e: %f" % (total_steps,np.mean(rList[-10:]), e))
saver.save(sess,path+'/model-'+str(i)+'.ckpt')
logDEBUG("Percent of succesful episodes: " + str(sum(rList)/num_episodes) + "%")
• And so, after 500000 steps we get a mean success rate of ~20% [actually, this is not a percentage, but rather a mean reward value!]:
2019-03-10T12:09:06.201518 [DEBUG] Total steps: 498500, mean value: 22.700000, e: 0.100000
2019-03-10T12:09:08.648956 [DEBUG] Total steps: 499000, mean value: 22.600000, e: 0.100000
2019-03-10T12:09:11.015712 [DEBUG] Total steps: 499500, mean value: 23.200000, e: 0.100000
2019-03-10T12:09:13.457738 [DEBUG] Total steps: 500000, mean value: 24.300000, e: 0.100000
2019-03-10T12:09:13.722211 [DEBUG] Percent of succesful episodes: 20.3113%
2019-03-10T12:09:13.784543 [DEBUG] Training completed in 2552.745198 seconds.
• So first we have this Qnetwork class:
• The class constructor takes as argument h_size: the final number of filters we want to get out of the convolutional layer 4
• So we have 4 conv layers, and then we split the output of the last conv layer with:
self.streamAC,self.streamVC = tf.split(self.conv4,2,3)
• The line above means that we are doing 2 splits, on the axis 3. The conv4 layer most probably has a structured output size of [-1, width, height, h_size], so if we split on axis 3, we get tensors of size [-1, width, height, h_size/2], so far so good.
• Next we convert from 4D tensors to standard 2D tensors (keeping the batch_size as first dimension, so we end with tensors of size ([batch_size, k]), using the slim.flatten call:
        self.streamA = slim.flatten(self.streamAC)
self.streamV = slim.flatten(self.streamVC)
• Then we continue using separated weights matrices and we compute the Advantage for each action on one split, and the state Value with the second split.
• Then we combine the value of the state and the advantage values together to build our Q(S,a) values (but first we substract the mean advantage value):
        self.Qout = self.Value + tf.subtract(self.Advantage,tf.reduce_mean(self.Advantage,axis=1,keep_dims=True))
self.predict = tf.argmax(self.Qout,1)
• Next we compute our loss values:
• We take a list of all actual target Q and a list of selected actions
• We convert those actions to a one shot action matrix with:
        self.actions = tf.placeholder(shape=[None],dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions,env.actions,dtype=tf.float32)
• Then we use a multiply trick to get the Q Value corresponding to the selected actions:
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis=1)
• And finally we proceed with a standard loss computation:
        self.td_error = tf.square(self.targetQ - self.Q)
self.loss = tf.reduce_mean(self.td_error)
• Next we have the experience_buffer class:
• So we have a buffer of a fixed size,
• We can add “experiences” to that buffer, ensuring that the size limit is respected.
• And then we can sample experiences randomly from that buffer.
• Then we have the updateTargetGraph function:
def updateTargetGraph(tfVars,tau):
total_vars = len(tfVars)
op_holder = []
for idx,var in enumerate(tfVars[0:total_vars//2]):
op_holder.append(tfVars[idx+total_vars//2].assign((var.value()*tau) + ((1-tau)*tfVars[idx+total_vars//2].value())))
return op_holder
• Concerning this one, we should notice that the tfVars argument contains all the trainable variables. But since we just create 2 identical networks, then we simply have 2 identical sets of variables: first set for main network, and second set for the target network.
• So what we do here is that we iterate on the first set of variables only, and using the value of those variables we update the values of the second set of variables. Another clever mechanism I'm just discovering here . That's great.
• Note that the value we use for tau is very small: tau=0.001, so the adaptation towards the primary network weights is very slow.
• And now we get to the actual training function:
• First we create our primary and target networks,
• We prepare an experience buffer
• Then we run a fixed number of episodes,
• For each step in the episode, we can either get a random action or get an action prediction from the primary network.
• We update the environment with that action to get a new state, reward, and done value
• And we save in our experience buffer the current experience: [state, action, reward, new_state, done]
• Then every 4 steps (ie. update_freq=4), we sample 32 experiences from our buffer (ie. batch_size=32)
• And we perform the Double-DQN update:
                        Q1 = sess.run(mainQN.predict,feed_dict={mainQN.scalarInput:np.vstack(trainBatch[:,3])})
Q2 = sess.run(targetQN.Qout,feed_dict={targetQN.scalarInput:np.vstack(trainBatch[:,3])})
• So we use the primary network to get a prediction of the action that should be selected for each “next_state” that we provide as input.
• Then we also run the target network on the same inputs to get all the predicted target Q values. And we select the QValue corresponding to the action selected in the main network.
• Then we have this “end_multiplier” thing: I'm not quite sure what this si about exactly, but basically we are going to multiply the Qvalue by 0.0 if the end of the episode was reached, and do nothing otherwise.
• Then we train our main network with the just computed target Q values, and we apply a very small part of the weight change into the target network. All good.
• blog/2019/0310_dqn.txt