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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Fundamental concepts of RL

In this section, we will learn about several important fundamental RL concepts.

Math essentials

Before going ahead, let's quickly recap expectation from our high school days, as we will be dealing with expectation throughout the book.


Let's say we have a variable X and it has the values 1, 2, 3, 4, 5, 6. To compute the average value of X, we can just sum all the values of X divided by the number of values of X. Thus, the average of X is (1+2+3+4+5+6)/6 = 3.5.

Now, let's suppose X is a random variable. The random variable takes values based on a random experiment, such as throwing dice, tossing a coin, and so on. The random variable takes different values with some probabilities. Let's suppose we are throwing a fair dice, then the possible outcomes (X) are 1, 2, 3, 4, 5, and 6 and the probability of occurrence of each of these outcomes is 1/6, as shown in Table 1.2:

Table 1.2: Probabilities of throwing...