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About Packt

Contributors

Preface

1. Introduction to Reinforcement Learning FREE CHAPTER

• What is RL?
• RL algorithm
• How RL differs from other ML paradigms
• Elements of RL
• Agent environment interface
• Types of RL environment
• RL platforms
• Applications of RL
• Summary
• Questions
• Further reading

2. Getting Started with OpenAI and TensorFlow

• Setting up your machine
• OpenAI Gym
• OpenAI Universe
• TensorFlow
• Summary
• Questions
• Further reading

3. The Markov Decision Process and Dynamic Programming

• The Markov chain and Markov process
• Markov Decision Process
• The Bellman equation and optimality
• Solving the Bellman equation
• Solving the frozen lake problem
• Summary
• Questions
• Further reading

4. Gaming with Monte Carlo Methods

• Monte Carlo methods
• Monte Carlo prediction
• Monte Carlo control
• Summary
• Questions
• Further reading

5. Temporal Difference Learning

• TD learning
• TD prediction
• TD control
• The difference between Q learning and SARSA
• Summary
• Questions
• Further reading

6. Multi-Armed Bandit Problem

• The MAB problem
• Applications of MAB
• Identifying the right advertisement banner using MAB
• Contextual bandits
• Summary
• Questions
• Further reading

7. Playing Atari Games

• Introduction to Atari games
• Building an Atari emulator
• Data preparation
• Deep Q-learning
• Implementation of DQN
• Experiments
• Summary

8. Atari Games with Deep Q Network

• What is a Deep Q Network?
• Architecture of DQN
• Building an agent to play Atari games
• Double DQN
• Prioritized experience replay
• Dueling network architecture
• Summary
• Questions
• Further reading

9. Playing Doom with a Deep Recurrent Q Network

• DRQN
• Training an agent to play Doom 
• DARQN
• Summary
• Questions
• Further reading

10. The Asynchronous Advantage Actor Critic Network

• The Asynchronous Advantage Actor Critic
• Driving up a mountain with A3C
• Summary
• Questions
• Further reading

11. Policy Gradients and Optimization

• Policy gradient
• Deep deterministic policy gradient
• Trust Region Policy Optimization
• Proximal Policy Optimization
• Summary
• Questions
• Further reading

12. Balancing CartPole

• OpenAI Gym
• Markov models
• Summary

13. Simulating Control Tasks

• Introduction to control tasks
• Deterministic policy gradient
• Trust region policy optimization
• Summary

14. Building Virtual Worlds in Minecraft

• Introduction to the Minecraft environment
• Data preparation
• Asynchronous advantage actor-critic algorithm
• Implementation of A3C
• Experiments
• Summary

15. Learning to Play Go

• A brief introduction to Go
• Monte Carlo tree search
• AlphaGo
• AlphaGo Zero
• Implementing AlphaGo Zero
• Summary
• References

16. Creating a Chatbot

• The background problem
• Step-by-step guide
• Summary

17. Generating a Deep Learning Image Classifier

• Neural Architecture Search
• Implementing NAS
• Advantages of NAS
• Summary

18. Predicting Future Stock Prices

• Background problem
• Data used
• Step-by-step guide
• Summary

19. Capstone Project - Car Racing Using DQN

• Environment wrapper functions
• Dueling network
• Replay memory
• Training the network
• Car racing
• Summary
• Questions
• Further reading

20. Looking Ahead

• The shortcomings of reinforcement learning
• Upcoming developments in reinforcement learning
• Summary
• References

1. Assessments

• Chapter 1: Introduction to Reinforcement Learning
• Chapter 2: Getting Started with OpenAI and TensorFlow
• Chapter 3: The Markov Decision Process and Dynamic Programming
• Chapter 4: Gaming with Monte Carlo Methods
• Chapter 5: Temporal Difference Learning
• Chapter 6: Multi-Armed Bandit Problem
• Chapter 8: Atari Games with Deep Q Network
• Chapter 9: Playing Doom with a Deep Recurrent Q Network
• Chapter 10: The Asynchronous Advantage Actor Critic Network
• Chapter 11: Policy Gradients and Optimization
• Chapter 19: Capstone Project – Car Racing Using DQN

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Index