Deep Reinforcement Learning in Action
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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.
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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 Policy: Policy Gradient Methods
4.1 Policy Function using Neural Networks
4.2 Reinforcing Good Actions: The Policy Gradient Algorithm
4.3 Working with OpenAI Gym
4.4 The REINFORCE Algorithm
4.5 Summary and what’s next
Tackling more Complex Environments with Actor-Critic Methods
Part 2: Above and beyond
6 Alternative Optimization Methods: Evolutionary Strategies
6.1 A Different Approach to Reinforcement Learning
6.2 Reinforcement Learning with Evolution Strategies
6.2.2 Selecting for the Fittest Agents
6.2.3 Recombining Agents to Produce New Agents
6.2.4 Introducing Mutations
6.2.5 Evolution takes multiple generations
6.2.6 Full training loop
6.3 Pros and Cons of Evolutionary Algorithms
6.3.1 Evolutionary Algorithms Explore More
6.3.2 Evolutionary algorithms are incredibly sample intensive
6.3.4 Gradient-Free Algorithms could faster to train
6.4 Evolutionary Strategies are Parallelizable
6.4.1 ES as a scalable alternative
6.4.2 Parallel vs Serial Processing
6.4.3 Scaling Efficiency
6.4.4 Communicating Between Nodes
6.4.5 Sending only two numbers
6.4.7 Scaling Linearly
6.4.8 Scaling Gradient Based Approaches
7 Understanding the environment and making plans: Model-Based Learning
8 Beyond inference: Reinforcement learning for creativity
9 Playing at your level: Self-Play
Appendix A: Mathematics
Appendix B: Deep Learning
Appendix C: PyTorch
Appendix D: Glossary
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.
Manning Early Access Program (MEAP) Read chapters as they are written, get the finished eBook as soon as it’s ready, and receive the pBook long before it's in bookstores.
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