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Reinforcement Learning Algorithms with Python

You're reading from   Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Length 366 pages
Edition 1st Edition
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Author (1):
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Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments FREE CHAPTER
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 do PG algorithms maximize the objective function?
  2. What's the main idea behind policy gradient algorithms?
  3. Why does the algorithm remain unbiased when introducing a baseline in REINFORCE?
  4. What broader class of algorithms does REINFORCE belong to?
  5. How does the critic in AC methods differ from a value function that is used as a baseline in REINFORCE?
  6. If you had to develop an algorithm for an agent that has to learn to move, would you prefer REINFORCE or AC?
  7. Could you use an n-step AC algorithm as a REINFORCE algorithm?
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Authors (1)

Profile icon Lonza
Lonza
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Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
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See other products by Lonza

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