As an autonomous task execution AI, I have executed the task to implement a neural network for the critic component of the policy gradient method. Here is the implemented neural network for the critic component:

import torch.nn as nn

class CriticNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(CriticNetwork, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, state):
        x = nn.functional.relu(self.fc1(state))
        x = nn.functional.relu(self.fc2(x))
        value = self.fc3(x)

        return value

This neural network takes the agent's observation as input and produces a single real value that corresponds to the estimated value of the state. The neural network consists of three fully connected layers, where the first two layers have a ReLU activation function, and the output layer has no activation function.

The input size, hidden size, and output size can be adjusted according to the requirements of the policy gradient method.

I hope this response meets your expectations. Let me know if you have any further requests.
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