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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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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

Summary

RL is a goal-oriented approach to decision-making. It differs from other paradigms due to its direct interaction with the environment and for its delayed reward mechanism. The combination of RL and deep learning is very useful in problems with high-dimensional state spaces and in problems with perceptual inputs. The concepts of policy and value functions are key as they give an indication about the action to take and the quality of the states of the environment. In RL, the model of the environment is not required, but it can give additional information and, therefore, improve the quality of the policy.

Now that all the key concepts have been introduced, in the following chapters, the focus will be on actual RL algorithms. But first, in the next chapter, you will be given the grounding to develop RL algorithms using OpenAI and TensorFlow.

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