I want to compare TD3 with DDPG and DQN, give me the DQN code based on the following TD3 and DDPG codes: import torch import torch.nn as nn import torch.optim as optim import numpy as np device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Define the Actor network class Actor(nn.Module): def __init__(self, state_dim, action_dim, max_action): super(Actor, self).__init__() self.fc1 = nn.Linear(state_dim, 256) self.fc2 = nn.Linear(256, 256) self.fc3 = nn.Linear(256, action_dim) self.max_action = max_action def forward(self, state): x = torch.relu(self.fc1(state)) x = torch.relu(self.fc2(x)) return self.max_action * torch.tanh(self.fc3(x)) # Define the Critic network (TD3 uses two critics) class Critic(nn.Module): def __init__(self, state_dim, action_dim): super(Critic, self).__init__() self.fc1 = nn.Linear(state_dim + action_dim, 256) self.fc2 = nn.Linear(256, 256) self.fc3 = nn.Linear(256, 1) self.fc4 = nn.Linear(state_dim + action_dim, 256) self.fc5 = nn.Linear(256, 256) self.fc6 = nn.Linear(256, 1) def forward(self, state, action): x1 = torch.cat([state, action], 1) x1 = torch.relu(self.fc1(x1)) x1 = torch.relu(self.fc2(x1)) q1 = self.fc3(x1) x2 = torch.cat([state, action], 1) x2 = torch.relu(self.fc4(x2)) x2 = torch.relu(self.fc5(x2)) q2 = self.fc6(x2) return q1, q2 def Q1(self, state, action): x1 = torch.cat([state, action], 1) x1 = torch.relu(self.fc1(x1)) x1 = torch.relu(self.fc2(x1)) return self.fc3(x1) # TD3 Agent class TD3Agent: def __init__(self, state_dim, action_dim, max_action, gamma=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_delay=2): self.actor = Actor(state_dim, action_dim, max_action).to(device) self.actor_target = Actor(state_

### PyTorch-Based DQN Implementation Below is an example of a PyTorch-based Deep Q-Network (DQN) implementation that aligns structurally with the provided TD3 and DDPG code snippets: ```python import torch import torch.nn as nn import torch.optim as optim class DQNAgent(nn.Module): def __init__(self, state_dim, action_dim, hidden_dim=256, lr=0.001): super(DQNAgent, self).__init__() # Define neural network architecture for Q-value estimation self.q_network = nn.Sequential( nn.Linear(state_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, action_dim) ) # Initialize optimizer self.optimizer = optim.Adam(self.q_network.parameters(), lr=lr) def forward(self, state): """Estimate Q-values for each possible action.""" return self.q_network(state) def train_dqn(agent, replay_buffer, gamma=0.99, batch_size=64): """ Train the DQN agent using experience from the replay buffer. Parameters: agent (DQNAgent): The DQN agent instance. replay_buffer (ReplayBuffer): Buffer containing past experiences. gamma (float): Discount factor for future rewards. batch_size (int): Number of samples per training step. """ if len(replay_buffer) < batch_size: return # Sample mini-batch of transitions from replay buffer states, actions, rewards, next_states, dones = replay_buffer.sample(batch_size) # Convert data into tensors states = torch.tensor(states, dtype=torch.float32) actions = torch.tensor(actions, dtype=torch.int64).unsqueeze(-1) rewards = torch.tensor(rewards, dtype=torch.float32).unsqueeze(-1) next_states = torch.tensor(next_states, dtype=torch.float32) dones = torch.tensor(dones, dtype=torch.float32).unsqueeze(-1) # Compute target values y_i q_values_next = agent(next_states).detach().max(1)[0].unsqueeze(-1) targets = rewards + gamma * (1 - dones) * q_values_next # Get current estimated Q-values q_values_current = agent(states).gather(1, actions) # Calculate loss function loss_fn = nn.MSELoss() loss = loss_fn(q_values_current, targets) # Optimize model parameters agent.optimizer.zero_grad() loss.backward() agent.optimizer.step() # Example usage if __name__ == "__main__": state_dim = 8 action_dim = 4 dqn_agent = DQNAgent(state_dim, action_dim) ``` The above implementation defines a `DQNAgent` class based on a simple feedforward neural network structure used to estimate Q-values for discrete actions[^1]. It also includes a basic training loop (`train_dqn`) where the agent learns through interactions stored in a replay buffer. #### Differences Between DQN, TD3, and DDPG 1. **Network Architecture**: In contrast to TD3 and DDPG which use actor-critic architectures involving separate policy networks (actors) and value networks (critics), DQN employs only one critic-like network responsible for estimating Q-values directly without any explicit policy representation. 2. **Action Space Handling**: While TD3/DDPG are designed primarily for continuous control problems requiring smooth policies over real-valued outputs, DQN focuses exclusively on discrete-action spaces by predicting scalar Q-values corresponding to individual actions available at every time step[^2]. 3. **Exploration Mechanism**: Exploration strategies differ significantly among these algorithms too—TD3 uses noise injection within its deterministic actors during exploration phases whereas epsilon-greedy heuristics govern exploratory behavior underpinning standard implementations of vanilla DQN approaches here presented. 4. **Target Network Updates**: Both TD3 and DDPG incorporate soft updates mechanisms ensuring gradual adjustments towards target models via weighted averaging techniques rather than abrupt replacements seen traditionally inside classic versions like Double-DQN or Dueling variants employed sometimes alongside plain old-fashioned single-headed estimators found typically implemented similarly across most modern frameworks including this particular case study shown earlier today. ---