Basic Q-Learning algorithm using Tensorflow

Q-Net Learning Clean.ipynb

Why are we updating both 0 index and a[0] index here?

targetQ[0,a[0]] = r + y*maxQ1

Thanks for articles and gists!

Very sorry, i thought targetQ was a vector basically, I now see that is 2 dimensional. Devil is in the details... Still, great stuff, excited to read the entire series.

Hi. Just starting the reinforcement learning tutorial. Is my understanding correct? Is the neural network trained upon every evaluation of the Q target values?

Looks like e = 1./((i/50) + 10) was intended to be e = 1./((i/50.) + 10), i is an integer and i/50 would be an integer division. Or do I miss smth?

@eviltnan You're right if you're on Python 2. The code will still run fine. But you can add in from __future__ import absolute_import, division, print_function

Why you dont' use replay memory and train network just after perfom action?

targetQ = allQ
targetQ[0,a[0]] = r + y*maxQ1

Does following code make a vector with previous Q values, but Q for action a - is new, calculated by Bellman equation, doesnt' it?

I tried to impement Q-Network with replay memory. But it doesn't work, play worse then just random inited weights. Something like that:

D = [] # replay memory

for i in range(1000):
   state = env.reset()
    
   for j in range(99):
       a = argmax(predict(s))    # predict returns Q(s, *) for all actions
       s1, reward, done, _ = env.step(a)
       D.appen((s, a, r, s1, done))      
       s = s1
       if done:
            break

    # now do replay
    batch = random.sample(D)
    for transition in batch:
         s, a, r, s1, done = transition
         expected_q = r + gamma * max(predict(s1))
         sess.run(train_step, {state: s, action: a, expected: expected_a})

So, i need to train my network to Q(s, a) -> r + gamma * max(Q(s1, *)). It's easy to calculate expected value. But for Q(s,a) I should
get my prediction for a, which is vector for all actions, and then peek action: predict[a].

Here it is:

expected = tf.placeholder(tf.float32, shape=())
action = tf.placeholder(tf.int32, shape=())
pr_reward = prediction[action0][0]  # prediction - this is network output
error = tf.square(reward0 - pr_reward)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(error)

I think, my problem, that error use only one predicted and expected values. Every example I see there was batch. I am new to NN and Tensorflow. Should loss function operate with vectors (batches)?

@awjuliani, about the line

_,W1 = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})

shouldn't this should be

_,W = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})

i.e. W instead of W1 ?? as we are wanting to update values of W

Is there anything that I am missing ??

@awjuliani, about the line

_,W1 = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})

shouldn't this should be

_,W = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})

i.e. W instead of W1 ?? as we are wanting to update values of W

Is there anything that I am missing ??

I thought this was odd too. The value of W is assigned once and never re-assigned, and a new variable W1 is created every iteration but never read. W1 seems to never do anything.

The W1 is not used, but left for visualization purposes for those interested in what the value is. The sess.run line is indeed updating W.

I made some additions to visualize the loss using tensorboard. Consider implementing these changes in the tutorial, as there is a good amount of confusion in the comments regarding the loss. Also would mention in the tutorial what version of python/tensorflow work for this tutorial (py 3.6.12 & tf 1.15). Enjoy!

import gym
import numpy as np
import random
import tensorflow as tf
import matplotlib.pyplot as plt
# %matplotlib inline
env = gym.make('FrozenLake-v0')

tf.reset_default_graph()
#These lines establish the feed-forward part of the network used to choose actions
inputs1 = tf.placeholder(shape=[1,16],dtype=tf.float32)
W = tf.Variable(tf.random_uniform([16,4],0,0.01))
Qout = tf.matmul(inputs1,W)
predict = tf.argmax(Qout,1) # best action (index)

#Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
nextQ = tf.placeholder(shape=[1,4],dtype=tf.float32)
loss = tf.reduce_sum(tf.square(nextQ - Qout)) 
lossSm = tf.summary.scalar("loss", loss) # scalar summary for tensorboard

trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
updateModel = trainer.minimize(loss)
init = tf.initialize_all_variables()

# Set learning parameters
y = .99
e = 0.1
num_episodes = 2000
#create lists to contain total rewards and steps per episode
jList = []
rList = []

with tf.Session() as sess:
    writer = tf.summary.FileWriter("log", sess.graph)

    sess.run(init)
    
    for i in range(num_episodes):
        #Reset environment and get first new observation
        s = env.reset()
        rAll = 0
        d = False
        j = 0
        #The Q-Network
        while j < 99:
            j+=1
            #Choose an action by greedily (with e chance of random action) from the Q-network
            a,allQ = sess.run([predict,Qout],feed_dict={inputs1:np.identity(16)[s:s+1]}) 
            if np.random.rand(1) < e:
                a[0] = env.action_space.sample()

            #Get new state and reward from environment
            s1,r,d,_ = env.step(a[0])
            
            #Obtain the Q' values by feeding the new state through our network
            Q1 = sess.run(Qout,feed_dict={inputs1:np.identity(16)[s1:s1+1]}) 
            
            #Obtain maxQ' and set our target value for chosen action.
            maxQ1 = np.max(Q1) # NOT argmax - just the max q-val
            targetQ = allQ # allQ = sess.run(Qout,s:s+1) -- prev q-vals
            targetQ[0,a[0]] = r + y*maxQ1 # a = sess.run(predict,s:s+1)  -- Bellman eq.
            
            #Train our network using target and predicted Q values
            # The W1 is not used, but left for visualization purposes for those interested 
            # in what the value is. The sess.run line is indeed updating W. - awjuliani
            lo,_,W1 = sess.run([lossSm,updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})
            rAll += r
            s = s1
            writer.add_summary(lo) # visualize loss fuction 
            if d == True:
                #Reduce chance of random action as we train the model.
                e = 1./((i/50) + 10)
                break
        jList.append(j)
        rList.append(rAll)
plt.plot(rList)

I've come across an oddity that I'm having trouble understanding. Running this code as written in Tensorflow works just fine for me, but trying to re-implement it in another framework (Pytorch, Keras) left me with a network that seemed unable to learn the game. It looks to me like the randomly initialized weights in the linear layer pass on bogus future reward estimates when the agent loses the game.
Explained another way, when the agent lost the game, instead of getting 0 reward for that action, it was getting 0 plus the max future reward for the "next step" of the game instead of just 0. I was able to get the agent to learn the game with this modification:

        if d == True:
            targetQ[0,a] = r
        else:
            targetQ[0,a] = r + y*maxQ1

Based on the explanation I've come up with, this modification makes perfect sense to me, but I'm left wondering why the example here does not have the same issue. Thoughts?
Reference code:

class FrozenLakeNet(nn.Module):
    def __init__(self):
        super(FrozenLakeNet,self).__init__()
        self.fc = nn.Linear(16,4,bias=False)
        self.fc.weight.data.uniform_(0,.01)
    def forward(self,xIn):
        x = self.fc(xIn)
        return(x)

# Create list of state vecotrs on device
states = []
device = torch.device('cuda:0')
for s in range(16):
    sv = torch.tensor(np.identity(16)[s:s+1].astype(np.float32))
    svG = sv.to(device)
    states.append(svG)

# Initialize network
net = FrozenLakeNet()
net.to(device)
# Setup loss
criterion = nn.MSELoss(reduction='sum')
# Setup optimizer
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0)

# Set learning parameters
y = .99
num_episodes = 50000
#create lists to contain total rewards and steps per episode
jList = []
rList = []
e = .25
randSel = 0
tot = 0
for i in range(num_episodes):
    #Reset environment and get first new observation
    s = env.reset()
    rAll = 0
    d = False
    j = 0
    sSeq = []
    # Set random epsilon for episode
    #eps = 1-(i/num_episodes)
    #The Q-Table learning algorithm
    while j < 99:
        j+=1
        # Zero gradients
        optimizer.zero_grad()
        #Choose an action by greedily (with e chance of random action) from the Q-network
        #a,allQ = sess.run([predict,Qout],feed_dict={inputs1:np.identity(16)[s:s+1]})
        allQ = net(states[s])
        # Convert the state to an action
        a = int(torch.argmax(allQ).cpu().detach())
        tot += 1
        if np.random.rand(1) < e:
            randSel+=1
            a = env.action_space.sample()
        #Get new state and reward from environment
        s1,r,d,_ = env.step(a)
        # Get predicted Q values from new state
        #Q1 = sess.run(Qout,feed_dict={inputs1:np.identity(16)[s1:s1+1]})
        Q1 = net(states[s1])
        # Get the value of the 'best' action from the network
        maxQ1 = torch.max(Q1)
        # Get the target Q from the initial state
        targetQ = allQ.clone()
        # Update the target Q with new information
        if d == True:
            targetQ[0,a] = r
        else:
            targetQ[0,a] = r + y*maxQ1 ### Using this reward regardless of "done" output results in not learning the game.
        #Train our network using target and predicted Q values
        #_,W1 = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})
        # Compute the loss
        loss = criterion(targetQ,allQ)
        # Compute gradients
        loss.backward()
        # Apply learnings
        optimizer.step()
        rAll += r
        s = s1
        
        if d == True:
            e = 1./((i/50.) + 4)
            break
    jList.append(j)
    rList.append(rAll)