Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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Overview of this book
Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.
The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (21 chapters)
Preface
Free Chapter
1. What is Reinforcement Learning?
2. OpenAI Gym
3. Deep Learning with PyTorch
4. The Cross-Entropy Method
5. Tabular Learning and the Bellman Equation
6. Deep Q-Networks
7. DQN Extensions
8. Stocks Trading Using RL
9. Policy Gradients – An Alternative
10. The Actor-Critic Method
11. Asynchronous Advantage Actor-Critic
12. Chatbots Training with RL
13. Web Navigation
14. Continuous Action Space
15. Trust Regions – TRPO, PPO, and ACKTR
16. Black-Box Optimization in RL
17. Beyond Model-Free – Imagination
18. AlphaGo Zero
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