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)

Improving DQNs with experience replay

The approximation of Q-values using neural networks with one sample at a time is not very stable. You will recall that, in FA, we incorporated experience replay to improve stability. Similarly, in this recipe, we will apply experience replay to DQNs.

With experience replay, we store the agent's experiences (an experience is composed of an old state, a new state, an action, and a reward) during episodes in a training session in a memory queue. Every time we gain sufficient experience, batches of experiences are randomly sampled from the memory and are used to train the neural network. Learning with experience replay becomes two phases: gaining experience, and updating models based on the past experiences randomly selected. Otherwise, the model will keep learning from the most recent experience and the neural network model could get stuck...