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)
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Deep Reinforcement Learning with Stable Baselines

So far, we have learned various deep reinforcement learning (RL) algorithms. Wouldn't it be nice if we had a library to easily implement a deep RL algorithm? Yes! There are various libraries available to easily build a deep RL algorithm.

One such popular deep RL library is OpenAI Baselines. OpenAI Baselines provides an efficient implementation of many deep RL algorithms, which makes them easier to use. However, OpenAI Baselines does not provide good documentation. So, we will look at the fork of OpenAI Baselines called Stable Baselines.

Stable Baselines is an improved implementation of OpenAI Baselines. Stable Baselines is easier to use and it also includes state-of-the-art deep RL algorithms along with several useful features. We can use Stable Baselines for quickly prototyping the RL model.

Let's start off the chapter by installing Stable Baselines, and then we will learn how to create our first agent...