<p>I am completely new to CNN's, and I do not quite know how to design or use them efficiently. That being said, I am attempting to build a CNN that learns to play Pac-man with reinforcement learning. I have trained it for about 3 hours and have seen little to no improvement. My observation space is 3 channels * 15 * 19, and there are 5 actions. Here is my code, I am open to any and all suggestions. Thanks for all your help.</p>

<pre><code>from minipacman import MiniPacman as pac
from torch import nn
import torch
import random
import torch.optim as optimal
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import keyboard


loss_fn = nn.MSELoss()
epsilon = 1
env = pac("regular", 1000)
time = 0
action = random.randint(0, 4)
q = np.zeros(3)
alpha = 0.01
gamma = 0.9
tick = 0
decay = 0.9999


class Value_Approximator (nn.Module):
    def __init__(self):
        super(Value_Approximator, self).__init__()
        # Convolution 1
        self.cnn1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.relu1 = nn.ReLU()

        # Max pool 1
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)

        # Convolution 2
        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)
        self.relu2 = nn.ReLU()

        # Max pool 2
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)

        # Fully connected 1 (readout)
        self.fc1 = nn.Linear(384, 5)

    def forward(self, x):
        # Convolution 1
        out = self.cnn1(x)
        out = self.relu1(out)

        # Max pool 1
        out = self.maxpool1(out)

        # Convolution 2
        out = self.cnn2(out)
        out = self.relu2(out)

        # Max pool 2
        out = self.maxpool2(out)

        # Resize
        # Original size: (100, 32, 7, 7)
        # out.size(0): 100
        # New out size: (100, 32*7*7)
        out = out.view(out.size(0), -1)

        # Linear function (readout)
        out = self.fc1(out)

        return out

approx = Value_Approximator()
optimizer = optimal.SGD(approx.parameters(), lr=alpha)


while time &lt; 50000:
    print("Time: "+str(time))
    print("Epsilon: "+str(epsilon))
    print()
    time += 1
    state = env.reset()
    tick = 0

    epsilon *= decay

    if epsilon &lt; 0.1:
        epsilon = 0.1

    while True:
        tick += 1
        state = np.expand_dims(state, 1)
        state = state.reshape(1, 3, 15, 19)
        q = approx.forward(torch.from_numpy(state))[0]

        if random.uniform(0, 1) &lt; epsilon:
            action = env.action_space.sample()
        else:
            _, action = torch.max(q, -1)
            action = action.item()
        new_state, reward, terminal, _ = env.step(action)
        show_state = new_state
        new_state = np.expand_dims(new_state, 1)
        new_state = state.reshape(1, 3, 15, 19)

        q_new = approx.forward(torch.from_numpy(new_state).type(torch.FloatTensor))[0]  # " find Q (s', a') "
        #  find optimal action Q value for next step
        new_max, _ = torch.max(q_new, -1)
        new_max = new_max.item()

        q_target = q.clone()
        q_target = Variable(q_target.data)

        #  update target value function according to TD
        q_target[action] = reward + torch.mul(new_max, gamma)  # " reward + gamma*(max(Q(s', a')) "

        loss = loss_fn(q, q_target)  # " reward + gamma*(max(Q(s', a')) - Q(s, a)) "
        # Update original policy according to Q_target ( supervised learning )
        approx.zero_grad()
        loss.backward()
        optimizer.step()

        #  Q and Q_target should converge
        if time % 100 == 0:
            state = torch.FloatTensor(show_state).permute(1, 2, 0).cpu().numpy()

            plt.subplot(131)
            plt.title("Imagined")
            plt.imshow(state)
            plt.subplot(132)
            plt.title("Actual")
            plt.imshow(state)
            plt.show(block=False)
            plt.pause(0.000001)

        if keyboard.is_pressed('1'):
            torch.save(approx.state_dict(), 'trained-10000.mdl')
        if keyboard.is_pressed('9'):
            torch.save(approx.state_dict(), 'trained-10000.mdl')

        if terminal or tick &gt; 100:
            plt.close()
            break

        state = new_state


torch.save(approx.state_dict(), 'trained-10000.mdl')
</code></pre>

CC BY-SA 4.0
2019-08-25T09:59:17.430Z
Are you implementing DQN algorithm? If yes, where did you study the algorithm from? Or did you wing it and implemented it like regular Q-learning with NN and hoped for the best. As far as I can see you didn't implement experience replay nor target network. You should read original DQN papers [here](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) and [here](http://files.davidqiu.com//research/nature14236.pdf)  , also there are numerous question asked about DQN on this site so you should dig through for more information.
Brale
CC BY-SA 4.0
2019-08-25T13:22:01.347Z
@Brale_ thanks for your reply! I implemented target network, and I’m pretty sure I did it right because it converges ok, and I didn’t think that experience replay was neccessary, is it? And I found a lot of solutions like making the network larger on here but none of them worked. I have also tried many different network configurations.
iamPres
CC BY-SA 4.0
2019-08-25T14:02:01.177Z
experience replay is a must have so you should implement that
Brale
CC BY-SA 4.0
2019-08-25T14:08:14.287Z
@Brale_ ok just to make sure if I wasn’t seeing any improvement whatsoever that could be attributed solely to not having experience replay?
iamPres
CC BY-SA 4.0
2019-08-25T14:33:58.547Z
not solely to that but a part of it yes
Brale
<deep-learning><reinforcement-learning><convolutional-neural-networks><deep-rl>
Torch CNN not training
iamPres
# Torch CNN not training

(Asked by: username_0 on 2019-08-24)

I am completely new to CNN's, and I do not quite know how to design or use them efficiently. That being said, I am attempting to build a CNN that learns to play Pac-man with reinforcement learning. I have trained it for about 3 hours and have seen little to no improvement. My observation space is 3 channels * 15 * 19, and there are 5 actions. Here is my code, I am open to any and all suggestions. Thanks for all your help.
from minipacman import MiniPacman as pac
from torch import nn
import torch
import random
import torch.optim as optimal
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import keyboard


loss_fn = nn.MSELoss()
epsilon = 1
env = pac("regular", 1000)
time = 0
action = random.randint(0, 4)
q = np.zeros(3)
alpha = 0.01
gamma = 0.9
tick = 0
decay = 0.9999


class Value_Approximator (nn.Module):
    def __init__(self):
        super(Value_Approximator, self).__init__()
        # Convolution 1
        self.cnn1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.relu1 = nn.ReLU()

        # Max pool 1
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)

        # Convolution 2
        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)
        self.relu2 = nn.ReLU()

        # Max pool 2
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)

        # Fully connected 1 (readout)
        self.fc1 = nn.Linear(384, 5)

    def forward(self, x):
        # Convolution 1
        out = self.cnn1(x)
        out = self.relu1(out)

        # Max pool 1
        out = self.maxpool1(out)

        # Convolution 2
        out = self.cnn2(out)
        out = self.relu2(out)

        # Max pool 2
        out = self.maxpool2(out)

        # Resize
        # Original size: (100, 32, 7, 7)
        # out.size(0): 100
        # New out size: (100, 32*7*7)
        out = out.view(out.size(0), -1)

        # Linear function (readout)
        out = self.fc1(out)

        return out

approx = Value_Approximator()
optimizer = optimal.SGD(approx.parameters(), lr=alpha)


while time < 50000:
    print("Time: "+str(time))
    print("Epsilon: "+str(epsilon))
    print()
    time += 1
    state = env.reset()
    tick = 0

    epsilon *= decay

    if epsilon < 0.1:
        epsilon = 0.1

    while True:
        tick += 1
        state = np.expand_dims(state, 1)
        state = state.reshape(1, 3, 15, 19)
        q = approx.forward(torch.from_numpy(state))[0]

        if random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()
        else:
            _, action = torch.max(q, -1)
            action = action.item()
        new_state, reward, terminal, _ = env.step(action)
        show_state = new_state
        new_state = np.expand_dims(new_state, 1)
        new_state = state.reshape(1, 3, 15, 19)

        q_new = approx.forward(torch.from_numpy(new_state).type(torch.FloatTensor))[0]  # " find Q (s', a') "
        #  find optimal action Q value for next step
        new_max, _ = torch.max(q_new, -1)
        new_max = new_max.item()

        q_target = q.clone()
        q_target = Variable(q_target.data)

        #  update target value function according to TD
        q_target[action] = reward + torch.mul(new_max, gamma)  # " reward + gamma*(max(Q(s', a')) "

        loss = loss_fn(q, q_target)  # " reward + gamma*(max(Q(s', a')) - Q(s, a)) "
        # Update original policy according to Q_target ( supervised learning )
        approx.zero_grad()
        loss.backward()
        optimizer.step()

        #  Q and Q_target should converge
        if time % 100 == 0:
            state = torch.FloatTensor(show_state).permute(1, 2, 0).cpu().numpy()

            plt.subplot(131)
            plt.title("Imagined")
            plt.imshow(state)
            plt.subplot(132)
            plt.title("Actual")
            plt.imshow(state)
            plt.show(block=False)
            plt.pause(0.000001)

        if keyboard.is_pressed('1'):
            torch.save(approx.state_dict(), 'trained-10000.mdl')
        if keyboard.is_pressed('9'):
            torch.save(approx.state_dict(), 'trained-10000.mdl')

        if terminal or tick > 100:
            plt.close()
            break

        state = new_state


torch.save(approx.state_dict(), 'trained-10000.mdl')

**Tags:** deep-learning, reinforcement-learning, convolutional-neural-networks, deep-rl

> **Comments:**
> **username_1**: Are you implementing DQN algorithm? If yes, where did you study the algorithm from? Or did you wing it and implemented it like regular Q-learning with NN and hoped for the best. As far as I can see you didn't implement experience replay nor target network. You should read original DQN papers [here](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) and [here](http://files.davidqiu.com//research/nature14236.pdf)  , also there are numerous question asked about DQN on this site so you should dig through for more information.  
> **username_0**: @username_1_ thanks for your reply! I implemented target network, and I’m pretty sure I did it right because it converges ok, and I didn’t think that experience replay was neccessary, is it? And I found a lot of solutions like making the network larger on here but none of them worked. I have also tried many different network configurations.  
> **username_1**: experience replay is a must have so you should implement that  
> **username_0**: @username_1_ ok just to make sure if I wasn’t seeing any improvement whatsoever that could be attributed solely to not having experience replay?  
> **username_1**: not solely to that but a part of it yes  

---

## 0 answers

