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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Forward propagation in ANNs

In this section, we will see how an ANN learns where neurons are stacked up in layers. The number of layers in a network is equal to the number of hidden layers plus the number of output layers. We don't take the input layer into account when calculating the number of layers in a network. Consider a two-layer neural network with one input layer, x, one hidden layer, h, and one output layer, y, as shown in the following diagram:

Figure 7.8: Forward propagation in ANN

Let's consider we have two inputs, x1 and x2, and we have to predict the output, . Since we have two inputs, the number of neurons in the input layer is two. We set the number of neurons in the hidden layer to four, and the number of neurons in the output layer to one. Now, the inputs are multiplied by weights, and then we add bias and propagate the resultant value to the hidden layer where the activation function is applied.

Before that, we need to initialize...