#### 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
Other Books You May Enjoy

# Training and tuning the network

In this recipe, we will train the DQN model to play Flappy Bird.

In each step of the training, we take an action following the epsilon-greedy policy: under a certain probability (epsilon), we will take a random action, flapping or not flapping in our case; otherwise, we select the action with the highest value. We also adjust the value of epsilon for each step as we favor more exploration at the beginning and more exploitation when the DQN model is getting more mature.

As we have seen, the observation for each step is a two-dimensional image of the screen. We need to transform the observation images into states. Simply using one image from a step will not provide enough information to guide the agent as to how to react. Hence, we form a state using images from four adjacent steps. We will first reshape the image into the expected size, then concatenate...