Description
by Maxim Lapan (Author)
New edition of the bestselling
guide to deep reinforcement learning and how it's used to solve complex
real-world problems. Revised and expanded to include multi-agent
methods, discrete optimization, RL in robotics, advanced exploration
techniques, and more
Key Features
Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms
Book Description
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.
What you will learn
Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft's TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik's Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network distillation techniques
Who this book is for
Some
fluency in Python is assumed. Sound understanding of the fundamentals
of deep learning will be helpful. This book is an introduction to deep
RL and requires no background in RL
Table of Contents
What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients – an Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld environment Web Navigation Continuous Action Space RL in Robotics Trust Regions – PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced exploration Beyond Model-Free – Imagination AlphaGo Zero RL in Discrete Optimisation Multi-agent RL