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)
26
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27
Index

The imagination-augmented agent

The overall idea of the new architecture, called imagination-augmented agent (I2A), is to allow the agent to imagine future trajectories using the current observations and incorporate these imagined paths into its decision process. The high-level architecture is shown in the following diagram:

Figure 22.1: The I2A architecture

The agent consists of two different paths used to transform the input observation: model-free and imagination. Model-free is a standard set of convolution layers that transforms the input image in high-level features. The other path, imagination, consists of a set of trajectories imagined from the current observation. The trajectories are called rollouts and they are produced for every available action in the environment. Every rollout consists of a fixed number of steps into the future, and on every step, a special model, called the environment model (EM) (but not to be confused with the expectation maximization method...