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Reinforcement Learning Algorithms with Python

You're reading from  Reinforcement Learning Algorithms with Python

Product type Book
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Pages 366 pages
Edition 1st Edition
Languages
Author (1):
Andrea Lonza Andrea Lonza
Profile icon Andrea Lonza

Table of Contents (19) Chapters

Preface 1. Section 1: Algorithms and Environments
2. The Landscape of Reinforcement Learning 3. Implementing RL Cycle and OpenAI Gym 4. Solving Problems with Dynamic Programming 5. Section 2: Model-Free RL Algorithms
6. Q-Learning and SARSA Applications 7. Deep Q-Network 8. Learning Stochastic and PG Optimization 9. TRPO and PPO Implementation 10. DDPG and TD3 Applications 11. Section 3: Beyond Model-Free Algorithms and Improvements
12. Model-Based RL 13. Imitation Learning with the DAgger Algorithm 14. Understanding Black-Box Optimization Algorithms 15. Developing the ESBAS Algorithm 16. Practical Implementation for Resolving RL Challenges 17. Assessments
18. Other Books You May Enjoy

Questions

  1. How can a policy neural network control a continuous agent?
  2. What's the KL divergence?
  3. What's the main idea behind TRPO?
  4. How is the KL divergence used in TRPO?
  5. What's the main benefit of PPO?
  6. How does PPO achieve good sample efficiency?
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