Deep Reinforcement Learning Hands-On - Second Edition (Code Files)

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.
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