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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

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  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
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  • Train an agent to win a car racing game using dueling DQN

Python Reinforcement Learning Projects
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ISBN: 978-1-78899-161-2

  • Train and evaluate neural networks...
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