Dqn-implementation-pytorch May 2026

Deep Q-Networks (DQN) combine Q-Learning with Deep Neural Networks to solve environments with high-dimensional state spaces. Implementing a robust DQN in PyTorch involves managing several moving parts: the neural network architecture, experience replay, target networks, and the training loop. 1. Define the Q-Network Architecture

The agent manages two identical networks: the (active learning) and the Target Network (stable targets). dqn-implementation-pytorch

The network approximates the Q-value function, mapping states to the expected rewards of each possible action. Deep Q-Networks (DQN) combine Q-Learning with Deep Neural

in a buffer. Sampling randomly from this buffer breaks the correlation between consecutive frames, which stabilizes training. : Usually 10510 to the fifth power 10610 to the sixth power transitions. Batch Size : Typically 32, 64, or 128. 3. The DQN Agent Logic Define the Q-Network Architecture The agent manages two

import torch import torch.nn as nn import torch.nn.functional as F class DQN(nn.Module): def __init__(self, state_dim, action_dim): super(DQN, self).__init__() # Simple MLP for low-dim states (e.g., CartPole) self.fc1 = nn.Linear(state_dim, 128) self.fc2 = nn.Linear(128, 128) self.fc3 = nn.Linear(128, action_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) return self.fc3(x) # Returns Q-values for all actions Use code with caution. Copied to clipboard 2. Implement Experience Replay Experience replay stores past transitions