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Deep Reinforcement Learning Hands-On Cover

Deep Reinforcement Learning Hands-On

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Paid access
|Feb 2020

Table of Contents

  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. Higher-Level RL libraries
  8. DQN Extensions
  9. Ways to Speed up RL
  10. Stocks Trading Using RL
  11. Policy Gradients
  12. The Actor-Critic Method
  13. Asynchronous Advantage Actor-Critic
  14. Training Chatbots with RL
  15. The TextWorld environment
  16. Web Navigation
  17. Continuous Action Space
  18. RL in Robotics
  19. Trust Regions
  20. Black-Box Optimization in RL
  21. Advanced exploration
  22. Beyond Model-Free
  23. AlphaGo Zero
  24. RL in Discrete Optimisation
  25. Multi-agent RL
PDF ISBN: 978-1-83882-004-6
Publisher: Packt Publishing Limited
Copyright owner: © 2020 Packt Publishing Limited
Publication date: 2020
Language: English
Pages: 826

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