#### Overview of this book

Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.
Preface
Free Chapter
Getting Started with Reinforcement Learning and PyTorch
Markov Decision Processes and Dynamic Programming
Monte Carlo Methods for Making Numerical Estimations
Capstone Project – Playing Flappy Bird with DQN
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# Tuning double DQN hyperparameters for CartPole

In this recipe, let's solve the CartPole environment using double DQNs. We will demonstrate how to fine-tune the hyperparameters in a double DQN to achieve the best performance.

In order to fine-tune the hyperparameters, we can apply the grid search technique to explore a set of different combinations of values and pick the one achieving the best average performance. We can start with a coarse range of values and continue to narrow it down gradually. And don’t forget to fix the random number generators for all of the following in order to ensure reproducibility:

• The Gym environment random number generator
• The epsilon-greedy random number generator
• The initial weights for the neural network in PyTorch

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