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)
26
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27
Index

I2A on Atari Breakout

The training path of I2A is a bit complicated and includes a lot of code and several steps. To understand it better, let's start with a brief overview. In this example, we will implement the I2A architecture described in the paper [2], adopted to the Atari environments, and test it on the Breakout game. The overall goal is to check the training dynamics and the effect of imagination augmentation on the final policy.

Our example consists of three parts, which correspond to different steps in the training:

  1. The baseline advantage actor-critic (A2C) agent in Chapter22/01_a2c.py. The resulting policy is used for obtaining observations of the EM.
  2. The EM training in Chapter22/02_imag.py. It uses the model obtained on the previous step to train the EM in an unsupervised way. The result is the EM weights.
  3. The final I2A agent training in Chapter22/03_i2a.py. In this step, we use the EM from step 2 to train a full I2A agent, which combines the model...