Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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The cross-entropy method on CartPole

The whole code for this example is in Chapter04/, but the following are the most important parts. Our model's core is a one-hidden-layer NN, with rectified linear unit (ReLU) and 128 hidden neurons (which is absolutely arbitrary). Other hyperparameters are also set almost randomly and aren't tuned, as the method is robust and converges very quickly.


We define constants at the top of the file and they include the count of neurons in the hidden layer, the count of episodes we play on every iteration (16), and the percentile of episodes' total rewards that we use for "elite" episode filtering. We will take the 70th percentile, which means that we will leave the top 30% of episodes sorted by reward.

class Net(nn.Module):
    def __init__(self, obs_size, hidden_size, n_actions):
        super(Net, self).__init__() = nn.Sequential...