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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Overview of the actor-critic method

The actor-critic method is one of the most popular algorithms in deep reinforcement learning. Several modern deep reinforcement learning algorithms are designed based on actor-critic methods. The actor-critic method lies at the intersection of value-based and policy-based methods. That is, it takes advantage of both value-based and policy-based methods.

In this section, without going into further detail, first, let's acquire a basic understanding of how the actor-critic method works and then, in the next section, we will get into more detail and understand the math behind the actor-critic method.

Actor-critic, as the name suggests, consists of two types of network—the actor network and the critic network. The role of the actor network is to find an optimal policy, while the role of the critic network is to evaluate the policy produced by the actor network. So, we can think of the critic network as a feedback network that...