Deep Reinforcement Learning - Hands-On (with Python). Book Review - Apply modern #RL methods to practical problems of #chatbots, #robotics, discrete optimization, we automation, and more. The second edition of this book includes multi-agent methods and advanced exploration techniques.
Apply modern #RL methods to practical problems of #chatbots, #robotics, discrete optimization, we automation, and more. The second edition of this book includes multi-agent methods and advanced exploration techniques.
The chapters of the book are: #1 - What is Reinforcement Learning? (Contains an introduction to RL ideas and the main formal models). #2. OpenAI Gym (Introduces the practical aspects of RL, using the open source library Gym) #3. Deep Learning with PyTorch (Gives a quick overview of the PyTorch library) #4. The Cross-Entropy Method (Introduces one of the simplest methods in RL to give you an impression of RL methods and problems) #5. Tabular Learning and the Bellman Equation (Introduces the value-based family of RL methods) #6. Deep Q-Networks (Describes deep Q-networks (DQNs), an extension of the value-based methods, allowing to solve a complicated environment. #7. Higher-Level RL Libraries (Describes the library PTAN, which you will use to simplify the implementations of RL methods) #8. DQN Extensions (Gives a detailed overview of a modern extension to the DQN method, to improve its stability and convergence in complex environment) #9. Ways to Speed up RL Methods (Provides an overview of ways to make the execution of RL code faster) #10. Stocks Trading Using RL (The first practical project and focuses on applying the DQN method to stock trading) #11. Policy Gradients - an Alternative (Introduces another family of RL methods that is based on policy learning) #12. The Actor-Critic Method (Describes one of the most widely used methods in RL) #13. Asynchronous Advantage Actor-Critic (Extends the actor-critic method with parallel environment communication, which improves stability and convergence) #14. Training Chatbots with RL (The second project and shows how to apply RL methods to natural language processing NLP problems) #15. The TextWorld Environment (Covers the application of RL methods to interactive fiction games) #16. Web Navigation (Another long project that applies RL to web page navigation using the MiniWoB set of tasks) #17. Continuous Action Space (Describes the specifics of environments using continuous action spaces and various methods) #18. RL in Robotics (Covers the application of RL methods to robotics problems, including small hardware robot) #19. Trust Regions - PPO, TRPO, ACKTR, and SAC (Continuous action spaces describing the trust region set of methods) #20.Black-Box Optimization of RL (Shows another set of methods that do not use gradients in their explicit form) #21. Advances Exploration (Covers different approaches that can be used for better exploration of the environment) #22. Beyond Model-Free - Imagination (Introduces the model-based approach to RL and uses recent research results about imagination in RL) #23. AlphaGo Zero (Describes the AlphaGo Zero method and applies it to the game Connect 4) #24. RL in Discrete Optimization (Describes the application of RL methods to the domain of discrete optimization, using the Rubik’s Cube as an environment) #25. Multi-agent RL (Introduces a relatively new direction of RL methods for situations with multiple agents)
The book includes 3 practical Reinforcement Learning project that you can build from scratch on your own with Python.
Example code files available on Github: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition Color Images of the book: https://static.packt-cdn.com/downloads/9781838826994_ColorImages.pdf
The content of this book review: 0:00 - Introduction 1:27 - Chapters 10:36 - How to use this book? 11:10 - Additional information (book price, etc.) 11:35 - Basic review and Final word
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