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.
A thorough introduction to Reinforcement Learning with an innovative approach to explain the math behind it.
Part 1: Foundations
1. What is Reinforcement Learning?
1.1. The Journey Here
1.2. Supervised and Unsupervised Learning
1.3. Problem Structuring in Control Tasks
1.4. The Standard Model
1.5. What can I do with Reinforcement Learning?
1.6. Why Deep Reinforcement Learning?
1.7. Why this book?
1.8. What’s next?
2. Modeling Reinforcement Learning Problems: Markov Decision Processes
2.1. String Diagrams and our teaching methods
2.2. Solving the Multi-Arm Bandit
2.3. Applying Bandits to Optimize Ad Placements
2.4. Building Networks with PyTorch
2.5. Solving Contextual Bandits
2.6. The Markov Property
2.7. Predicting Future Rewards: Value and Policy Functions
2.8. Chapter Summary
2.9. What’s next?
3. Predicting the Best States and Actions: Deep Q-Networks
3.1. The Q-function
3.2. Navigating with Q-learning
3.3. Preventing Catastrophic Forgetting: Experience Replay
3.4. Improving Stability with a Target Network
3.6. What’s next?
4. Learning to Pick the Best Action: Policy Gradients
5. Understanding the environment and making plans: Model-Based Learning
Part 2: Above and beyond
6. Build your own AlphaGo
7. Alternative Optimization Methods: Evolutionary Methods
8. Reinforcement Learning for Vehicle Routing: Combinatorial Optimization
9. Strategies for Continual Learning: Defeating Catastrophic Forgetting
10. Beyond inference: Reinforcement learning for creativity
Appendix A: Mathematics
Appendix B: Deep Learning
Appendix C: PyTorch
Appendix D: Glossary
About the TechnologyDeep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! More exciting applications wait to be discovered. Let’s get started.
About the bookDeep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on direct feedback from their environment. You’ll build networks with the popular PyTorch deep learning framework to explore reinforcement learning algorithms ranging from Deep Q-Networks to Policy Gradients methods to Evolutionary Algorithms. As you go, you’ll apply what you know to hands-on projects like controlling simulated robots, automating stock market trades, and even building a bot to play Go.
- Structuring problems as Markov Decision Processes
- Popular algorithms such Deep Q-Networks, Policy Gradient method and
- Evolutionary Algorithms and the intuitions that drive them
- Applying reinforcement learning algorithms to real-world problems
About the authorsAlexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet which powers a suite of AWS machine learning products. He is also the cofounder of Codesmith, a software engineering bootcamp with residencies in Los Angeles and New York. Brandon Brown is a UCSF medical student and Data Scientist at UCLA. He has blogged extensively about machine learning on outlace.com for the past three years.
A very great book that takes you through deep reinforcement learning with interesting use cases and applications from the ground up.
Great introduction to the concepts of deep reinforcement learning in an easy to read package.
Great overview of deep learning while exposing under the hood implementation via PyTorch and actually tuning it. Also great theory and gentle math to make it stick.
I was little bit familiar with deep learning before and was curious about deep reinforcement learning for while, but yet not found end-to-end manual not overload with math or too simplistic and practical the other hand. Your book is just what I was looking for.