000			01926nam a2200229Ia 4500
003			NULRC
005			20250520103029.0
008			250520s9999 xx 000 0 und d
020			_a9789355512055
040			_cNULRC
050			_aQA 76.73.P98 .G75 2022
100			_aGridin, Ivan _eauthor
245		0	_aPractical deep reinforcement learning with python / _cIvan Gridin
260			_aDelhi : _bBPB Publications, _cc2022
300			_axx, 377 pages : _billustrations ; _c24 cm.
365			_bUSD30
504			_aIncludes index.
505			_a1. Introducing Reinforcement Learning -- 2. Playing Monopoly and Markov Decision Process -- 3. Training in Gym -- 4. Struggling with Multi- Armed Bandits -- 5. Blackjack in Monte Carlo -- 6. Escaping Maze with Q-Learning -- 7. Discretization -- Part II. Deep Reinforcement Learning -- 8. TensorFlow, PyTorch, and Your First Neural Network -- 9. Deep Q-Network and Lunar Lander -- 10. Defending Atlantis With Double Deep Q-Network -- 11. From Q-Learning to Policy-Gradient -- 12. Stock Trading With Actor-Critic -- 13. What Is Next?.
520			_aThis book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained.
650			_aPYTHON (COMPUTER PROGRAM LANGUAGE)
942			_2lcc _cBK
999			_c21786 _d21786