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

Imitation Learning and Inverse RL

Learning from demonstration is often called imitation learning. In the imitation learning setting, we have expert demonstrations and train our agent to mimic those expert demonstrations. Learning from demonstrations has many benefits, including helping an agent to learn more quickly. There are several approaches to perform imitation learning, and two of them are supervised imitation learning and Inverse Reinforcement Learning (IRL).

First, we will understand how we can perform imitation learning using supervised learning, and then we will learn about an algorithm called Dataset Aggregation (DAgger). Next, we will learn how to use demonstration data in a DQN using an algorithm called Deep Q Learning from Demonstrations (DQfD).

Moving on, we will learn about IRL and how it differs from reinforcement learning. We will learn about one of the most popular IRL algorithms called maximum entropy IRL. Toward the end of the chapter, we will understand...