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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
Concepts
Author (1):
Andrea Lonza Andrea Lonza
Profile icon Andrea Lonza
LinkedIn
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.
See other products by 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

The core of EAs

EAs are inspired by biological evolution and implement techniques and mechanisms that simulate biological evolution. This means that EAs go through many trials to create a population of new candidate solutions. These solutions are also called individuals (in RL problems, a candidate solution is a policy) that are better than the previous generation, in a similar way to the process within nature wherein only the strongest survive and have the possibility to procreate.

One of the advantages of EAs is that they are derivative-free methods, meaning that they don't use the derivative to find the solution. This allows EAs to work very well with all sorts of differentiable and non-differentiable functions, including deep neural networks. This combination is schematized in the following diagram. Note that each individual is a separate deep neural network, and so...

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Authors (1)

Profile icon Andrea Lonza
LinkedIn
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.
See other products by Andrea Lonza

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