#### 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 SARSA

In this recipe, we will solve the Taxi environment with the SARSA algorithm and fine-tune the hyperparameters with the grid search algorithm.

We will start with our default set of hyperparameter values under the SARSA model. These are selected based on intuition and a number of trials. Moving on, we will come up with the best set of values.

# How to do it...

We perform SARSA to solve the Taxi environment as follows:

1. Import PyTorch and the gym module, and create an instance of the Taxi environment:
`>>> import torch>>> import gym>>> env = gym.make('Taxi-v2')`
1. Then, start defining the epsilon-greedy behavior policy. We will reuse the gen_epsilon_greedy_policy...