Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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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MountainCar experiments

In this section, we will try to implement and compare the effectiveness of different exploration approaches on a simple, but still challenging, environment, which could be classified as a "classical RL" problem that is very similar to the familiar CartPole. But in contrast to CartPole, the MountainCar problem is quite challenging from an exploration point of view.

The problem's illustration is shown in the following figure and it consists of a small car starting from the bottom of the valley. The car can move left and right, and the goal is to reach the top of the mountain on the right.

Figure 21.3: The MountainCar environment

The trick here is in the environment's dynamics and the action space. To reach the top, the actions need to be applied in a particular way to swing the car back and forth to speed it up. In other words, the agent needs to apply the actions for several time steps to make the car go faster and eventually...