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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

Future of RL and its impact on society

The first foundations of AI were built more than 50 years ago, but only in the last few years has the innovation brought by AI spread through the world as a mainstream technology. This new wave of innovation is mainly due to the evolution of deep neural networks in supervised learning systems. However, the most recent breakthrough in artificial intelligence involves reinforcement learning, and most notably, deep reinforcement learning. Results like the ones that were obtained in the game of Go and Dota highlight the impressive quality of RL algorithms that are able to show long-term planning, ability in teamwork, and discover new game strategies that are difficult to comprehend even for humans.

The remarkable results that were obtained in the simulated environments started a new wave of applications of reinforcement learning in the physical...

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