LEADER 03191nam 22006015 450 001 9910983040903321 005 20250122120756.0 010 $a9789819739448 024 7 $a10.1007/978-981-97-3944-8 035 $a(CKB)37313186400041 035 $a(MiAaPQ)EBC31885441 035 $a(Au-PeEL)EBL31885441 035 $a(DE-He213)978-981-97-3944-8 035 $a(OCoLC)1488794836 035 $a(EXLCZ)9937313186400041 100 $a20250122d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical Foundations of Reinforcement Learning /$fby Shiyu Zhao 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (283 pages) 311 08$a9789819739431 327 $a1 Basic Concepts -- 2 State Value and Bellman Equation -- 3 Optimal State Value and Bellman Optimality Equation -- 4 Value Iteration and Policy Iteration -- 5 Monte Carlo Learning -- 6 Stochastic Approximation -- 7 Temporal-Difference Learning -- 8 Value Function Approximation -- 9 Policy Gradient -- 10 Actor-Critic Methods. 330 $aThis book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability. The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods. With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning. 606 $aArtificial intelligence 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aMultiagent systems 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aData Science 606 $aMultiagent Systems 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aMultiagent systems. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aData Science. 615 24$aMultiagent Systems. 676 $a006.3 700 $aZhao$b Shiyu$01785025 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983040903321 996 $aMathematical Foundations of Reinforcement Learning$94316642 997 $aUNINA