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)
Other Books You May Enjoy

Putting it all together

Putting all the concepts we have learned so far together, we will see how to build a neural network from scratch. We will understand how the neural network learns to perform the XOR gate operation. The XOR gate returns 1 only when exactly only one of its inputs is 1, else it returns 0, as shown in Table 7.1:

Table 7.1: XOR operation

Building a neural network from scratch

To perform the XOR gate operation, we build a simple two-layer neural network, as shown in the following diagram. As you can see, we have an input layer with two nodes, a hidden layer with five nodes and an output layer comprising one node:

Figure 7.13: ANN

We will understand step-by-step how a neural network learns the XOR logic:

  1. First, import the libraries:
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
  2. Prepare the data as shown in the preceding XOR table:
    X = np.array([ [0, 1], [1, 0], [1, 1],[0...