000			02976nam a2200217Ia 4500
003			NULRC
005			20250520102954.0
008			250520s9999 xx 000 0 und d
020			_a9781484251263
040			_cNULRC
050			_aQ 325.6 .B49 2019
100			_aBeysolow II, Taweh _eauthor
245		0	_aApplied reinforcement learning with Python : _bwith OpenAI Gym, Tensorflow and Keras / _cTaweh Beysolow II.
260			_a[Berkely, California] : _bApress, _cc2019
300			_axv, 168 pages ; _c24 cm.
504			_aIncludes bibliographical references and index.
505			_aChapter 1: Introduction to Reinforcement LearningChapter Goal: Inform the reader of the history of the field, its current applications, as well as generally discussing the outline of the text and what the reader can expect to learn No of pages 10Sub -Topics1. What is reinforcement learning? 2. History of reinforcement learning 3. Applications of reinforcement learning Chapter 2: Reinforcement Learning AlgorithmsChapter Goal: Establishing an understanding with the reader about how reinforcement learning algorithms work and how they differ from basic ML/DL methods. Practical examples to be provided for this chapter No of pages: 50 Sub -- Topics 1. Tabular solution methods2. Approximate solution methods Chapter 3: Q Learning Chapter Goal: In this chapter, readers will continue to build on their understanding of RL by solving problems in discrete action spaces No of pages : 40 Sub -- Topics: 1. Deep Q networks2. Double deep Q learning Chapter 4: Reinforcement Learning Based Market Making Chapter Goal: In this chapter, we will focus on a financial based use case, specifically market making, in which we must buy and sell a financial instrument at any given price. We will apply a reinforcement learning approach to this data set and see how it performs over time No of pages: 50Sub -- Topics: 1. Market making 2. AWS/Google Cloud3. Cron Chapter 5: Reinforcement Learning for Video Games Chapter Goal: In this chapter, we will focus on a more generalized use case of reinforcement learning in which we teach an algorithm to successfully play a game against computer based AI. No of pages: 50Sub -- Topics: 1. Game background and data collection.
520			_aDelve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.
650			_aOPENAI GYM
942			_2lcc _cBK
999			_c20227 _d20227