Deep RL Setup Tutorial

This guide will walk you through the setup process for Deep Reinforcement Learning (Deep RL). If you're looking for a more detailed explanation, check out our Deep RL Tutorial.

Prerequisites

Python 3.x
Anaconda or Miniconda
CUDA and cuDNN (if you plan to use GPU acceleration)

Installation

Install Anaconda or Miniconda.
Create a new environment:
```
conda create -n deep_rl python=3.8
```
Activate the environment:
```
conda activate deep_rl
```

Install required packages:

conda install gym matplotlib numpy pytorch torchvision

Environment Setup

Download the OpenAI Gym environment you want to use for your experiments. For example, the cartpole environment:
```
git clone https://github.com/openai/gym.git
cd gym/envs/classic_control/cart_pole
python setup.py install
```
To use GPU acceleration, make sure you have the appropriate CUDA and cuDNN versions installed.

Sample Code

Here's a simple example of a Deep RL setup using PyTorch:

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

# Define the neural network
class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, action_size)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

# Create the environment
env = gym.make('CartPole-v1')

# Initialize the network
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
q_network = QNetwork(state_size, action_size)

# Define the optimizer and loss function
optimizer = optim.Adam(q_network.parameters(), lr=0.001)
loss_function = nn.MSELoss()

# Your training loop here

For more information on how to train the network, check out our Deep RL Training Guide.