000			02033nam a22002897a 4500
003			OSt
005			20250122142245.0
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020			_a9781617295430
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082	0	0	_a004.85 _bZAI-D
100	1		_aZai, Alexander
245	1	0	_aDeep reinforcement learning in action
260			_aShelter Island, NY : _bManning Publications Company, _c2020
300			_axx, 359 pages ; _c23 cm
504			_aIncludes bibliographical references (pages 348-350) and index.
520			_aHumans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.
521			_aFor readers with intermediate skills in Python and deep learning.
650		0	_aMachine learning.
650		0	_aAlgorithms.
650		0	_aReinforcement learning.
650		0	_aComputational learning theory.
650		0	_aArtificial intelligence.
700	1		_aBrown, Brandon
856			_3Reviews _uhttps://www.goodreads.com/book/show/50075895-deep-reinforcement-learning-in-action?ref=nav_sb_ss_1_13#CommunityReviews
942			_2udc _cBK
999			_c35546 _d35546