#### 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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# Setting up the Windy Gridworld environment playground

In the previous recipe, we solved a relatively simple environment where we can easily obtain the optimal policy. In this recipe, let's simulate a more complex grid environment, Windy Gridworld, where an external force moves the agent from certain tiles. This will prepare us to search for the optimal policy using the TD method in the next recipe.

Windy Gridworld is a grid problem with a 7 * 10 board, which is displayed as follows:

An agent makes a move up, right, down, and left at a step. Tile 30 is the starting point for the agent, and tile 37 is the winning point where an episode will end if it is reached. Each step the agent takes incurs a -1 reward.

The complexity in this environment is that there is extra wind force in columns 4 to 9. Moving from tiles on those columns, the agent will experience an extra push upward...