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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Variables, constants, and placeholders

Variables, constants, and placeholders are fundamental elements of TensorFlow. However, there is always confusion between these three. Let's look at each element, one by one, and learn the difference between them.


Variables are containers used to store values. Variables are used as input to several other operations in a computational graph. A variable can be created using the tf.Variable() function, as shown in the following code:

x = tf.Variable(13)

Let's create a variable called W, using tf.Variable(), as follows:

W = tf.Variable(tf.random_normal([500, 111], stddev=0.35), name="weights")

As you can see in the preceding code, we create a variable, W, by randomly drawing values from a normal distribution with a standard deviation of 0.35.

What is that name parameter in tf.Variable()?

It is used to set the name of the variable in the computational graph. So, in the preceding code...