Book Image

PyTorch 1.x Reinforcement Learning Cookbook

By : Yuxi (Hayden) Liu
Book Image

PyTorch 1.x Reinforcement Learning Cookbook

By: Yuxi (Hayden) Liu

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.
Table of Contents (11 chapters)

Developing deep Q-networks

You will recall that Function Approximation (FA) approximates the state space using a set of features generated from the original states. Deep Q-Networks (DQNs) are very similar to FA with neural networks, but they use neural networks to map the states to action values directly instead of using a set of generated features as media.

In Deep Q-learning, a neural network is trained to output the appropriate Q(s,a) values for each action given the input state, s. The action, a, of the agent is chosen based on the output Q(s,a) values following the epsilon-greedy policy. The structure of a DQN with two hidden layers is depicted in the following diagram:

You will recall that Q-learning is an off-policy learning algorithm and that it updates the Q-function based on the following equation:

Here, s' is the resulting state after taking action, a, in state...