#### 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 Mountain Car environment playground

The TD method can learn the Q-function during an episode but is not scalable. For example, the number of states in a chess game is around 1,040, and 1,070 in a Go game. Moreover, it seems infeasible to learn the values for continuous state using the TD method. Hence, we need to solve such problems using function approximation (FA), which approximates the state space using a set of features.

In this first recipe, we will begin by getting familiar with the Mountain Car environment, which we will solve with the help of FA methods in upcoming recipes.

Mountain Car (https://gym.openai.com/envs/MountainCar-v0/) is a typical Gym environment with continuous states. As shown in the following diagram, its goal is to get the car to the top of the hill:

On a one-dimensional track, the car is positioned between -1.2 (leftmost) and 0.6 (rightmost...