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)

Solving the continuous Mountain Car environment with the advantage actor-critic network

In this recipe, we are going to solve the continuous Mountain Car problem using the advantage actor-critic algorithm, a continuous version this time of course. You will see how it differs from the discrete version.

As we have seen in A2C for environments with discrete actions, we sample actions based on the estimated probabilities. How can we model a continuous control, since we can't do such sampling for countless continuous actions? We can actually resort to Gaussian distribution. We can assume that the action values are under a Gaussian distribution:

Here, the mean, , and deviation, , are computed from the policy network. With this tweak, we can sample actions from the constructed Gaussian distribution by current mean and deviation. The loss function in continuous A2C is similar to...