#### 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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# Solving the Taxi problem with Q-learning

The Taxi problem (https://gym.openai.com/envs/Taxi-v2/) is another popular grid world problem. In a 5 * 5 grid, the agent acts as a taxi driver to pick up a passenger at one location and then drop the passenger off at their destination. Take a look at the following example:

Colored tiles have the following meanings:

• Yellow: The starting position of the taxi. The starting location is random in each episode.
• Blue: The position of the passenger. It is also randomly selected in each episode.
• Purple: The destination of the passenger. Again, it is randomly selected in each episode.
• Green: The position of the taxi with the passenger.

The four letters R, Y, B, and G indicate the only tiles that allow picking up and dropping off the passenger. One of them is the destination, and one is where the passenger is located.

The taxi can take the following...