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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
Languages
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Author (1):
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Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments
2. The Landscape of Reinforcement Learning FREE CHAPTER 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

Categorizing RL algorithms

Before deep diving into the first RL algorithm that solves the optimal Bellman equation, we want to give a broad but detailed overview of RL algorithms. We need to do this because their distinctions can be quite confusing. There are many parts involved in the design of algorithms, and many characteristics have to be considered before deciding which algorithm best fits the actual needs of the user. The scope of this overview presents the big picture of RL so that in the next chapters, where we'll give a comprehensive theoretical and practical view of these algorithms, you will already see the general objective and have a clear idea of their location in the map of RL algorithms.

The first distinction is between model-based and model-free algorithms. As the name suggests, the first requires a model of the environment, while the second is free from...

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