Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

The Asynchronous Advantage Actor Critic

The A3C network came as a storm and took over the DQN. Aside of the previously stated advantages, it also yields good accuracy compared to other algorithms. It works well in both continuous and discrete action spaces. It uses several agents, and each agent learns in parallel with a different exploration policy in copies of the actual environment. Then, the experience obtained from these agents is aggregated to the global agent. The global agent is also called a master network or global network and other agents are also called the workers. Now, we will see in detail how A3C works and how it differs from the DQN algorithm.

The three As

Before diving in, what does A3C mean? What do the...