Book Image

Deep Reinforcement Learning with Python - Second Edition

By : Sudharsan Ravichandiran
Book Image

Deep Reinforcement Learning with Python - Second Edition

By: Sudharsan Ravichandiran

Overview of this book

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
Table of Contents (22 chapters)
18
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Index

Distributional Reinforcement Learning

In this chapter, we will learn about distributional reinforcement learning. We will begin the chapter by understanding what exactly distributional reinforcement learning is and why it is useful. Next, we will learn about one of the most popular distributional reinforcement learning algorithms called categorical DQN. We will understand what a categorical DQN is and how it differs from the DQN we learned in Chapter 9, Deep Q Networks and Its Variants, and then we will explore the categorical DQN algorithm in detail.

Following this, we will learn another interesting algorithm called Quantile Regression DQN (QR-DQN). We will understand what a QR-DQN is and how it differs from a categorical DQN, and then we will explore the QR-DQN algorithm in detail.

At the end of the chapter, we will learn about the policy gradient algorithm called the Distributed Distributional Deep Deterministic Policy Gradient (D4PG). We will learn what the D4PG...