#### 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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# Developing Dueling deep Q-Networks

In this recipe, we are going to develop another advanced type of DQNs, Dueling DQNs (DDQNs). In particularly, we will see how the computation of the Q value is split into two parts in DDQNs.

In DDQNs, the Q value is computed with the following two functions:

Here, V(s) is the state-value function, calculating the value of being at state s; A(s, a) is the state-dependent action advantage function, estimating how much better it is to take an action, a, rather than taking other actions at a state, s. By decoupling the value and advantage functions, we are able to accommodate the fact that our agent may not necessarily look at both the value and advantage at the same time during the learning process. In other words, the agent using DDQNs can efficiently optimize either or both functions as it prefers.

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