000			a
999			_c33374 _d33374
008			241114b xxu||||| |||| 00| 0 eng d
020			_a9789811906374
082			_a006.31 _bPLA
100			_aPlaat, Aske
245			_aDeep reinforcement learning
260			_bSpringer, _c2022 _aSingapore :
300			_axiv, 406 p. ; _bill., _c25 cm.
365			_b3240.00 _c₹ _d01
504			_aIncludes bibliographical references and index.
520			_aDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the worlds leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
650			_aHuman-computer interaction
650			_aReinforcement learning
650			_aClimate change
650			_aConditional probability
650			_aExtraneous variables
650			_aNull hypothesis
650			_aProbability distribution
650			_aSexual selection
650			_aStandard deviation
942			_2ddc _cBK