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DQN PyTorch Beginners Tutorial #5 – Implement Target Network
In the fifth tutorial of our DQN PyTorch series, we will be implementing a Target Network to improve the stability and performance of our Deep Q-Network (DQN) algorithm.
Target Networks are often used in reinforcement learning algorithms, such as DQN, to provide a stable target for the Q-values during training. This can help prevent the algorithm from oscillating or diverging during training.
To implement a Target Network in PyTorch, we will create a separate model that will be used to update the target Q-values periodically. This helps to reduce the correlation between the target Q-values and the predicted Q-values, which can improve the stability of the algorithm.
Here is a simple implementation of a Target Network in PyTorch:
import torch
import torch.nn as nn
import torch.optim as optim
class DQN(nn.Module):
def __init__(self, input_size, output_size):
super(DQN, self).__init__()
self.fc1 = nn.Linear(input_size, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
class TargetDQN(nn.Module):
def __init__(self, input_size, output_size):
super(TargetDQN, self).__init__()
self.fc1 = nn.Linear(input_size, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
In this implementation, we have defined a separate TargetDQN class that has the same architecture as our original DQN model. We will use this TargetDQN model to update the target Q-values periodically by copying the weights from the original DQN model.
Now, let’s see how we can update the target Q-values in our training loop:
target_update_freq = 1000
for i in range(num_episodes):
...
if i % target_update_freq == 0:
target_dqn.load_state_dict(dqn.state_dict())
Here, we update the target Q-values every `target_update_freq` episodes by copying the weights from the original DQN model to the TargetDQN model using the `load_state_dict` method.
By implementing a Target Network in our DQN algorithm, we can improve the stability and performance of our model and achieve better results in our reinforcement learning tasks. Try implementing a Target Network in your own DQN PyTorch implementation and see the improvements in your results!
Fantastic
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