Policy Gradient with PyTorch

Introduction to Policy Gradient

Discover the fascinating world of policy gradient methods in reinforcement learning. Explore how these techniques allow agents to make decisions by learning policies that optimize expected rewards.

Getting Started

Firstly, ensure you have PyTorch installed. You can follow the installation guide for a step-by-step walkthrough.

import torch
import torch.nn as nn
import torch.optim as optim

Example Code

Here is a simple implementation of a policy gradient algorithm using PyTorch:

class PolicyGradientNetwork(nn.Module):
    def __init__(self):
        super(PolicyGradientNetwork, self).__init__()
        self.layer1 = nn.Linear(state_size, hidden_size)
        self.layer2 = nn.Linear(hidden_size, action_size)

    def forward(self, x):
        x = torch.relu(self.layer1(x))
        return torch.softmax(self.layer2(x), dim=1)

Read more about policy gradients in our documentation.

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