Deep Reinforcement Learning in Action

Summary

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.

About the technology

Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error.

About the book

Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.

What's inside

Building and training DRL networks
The most popular DRL algorithms for learning and problem solving
Evolutionary algorithms for curiosity and multi-agent learning
All examples available as Jupyter Notebooks

About the reader

For readers with intermediate skills in Python and deep learning.

About the author

Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger.

Table of Contents

PART 1 - FOUNDATIONS

1. What is reinforcement learning?

2. Modeling reinforcement learning problems: Markov decision processes

3. Predicting the best states and actions: Deep Q-networks

4. Learning to pick the best policy: Policy gradient methods

5. Tackling more complex problems with actor-critic methods

PART 2 - ABOVE AND BEYOND

6. Alternative optimization methods: Evolutionary algorithms

7. Distributional DQN: Getting the full story

8.Curiosity-driven exploration

9. Multi-agent reinforcement learning

10. Interpretable reinforcement learning: Attention and relational models

11. In conclusion: A review and roadmap