LEADER 04429nam 2200721 450 001 9910133842403321 005 20221206100129.0 024 7 $a10.1109/9780470544785 035 $a(CKB)3280000000033540 035 $a(SSID)ssj0000403147 035 $a(PQKBManifestationID)12190227 035 $a(PQKBTitleCode)TC0000403147 035 $a(PQKBWorkID)10431497 035 $a(PQKB)10540472 035 $a(CaBNVSL)mat05273582 035 $a(IDAMS)0b000064810d1139 035 $a(IEEE)5273582 035 $a(OCoLC)798698949 035 $a(EXLCZ)993280000000033540 100 $a20151221d2004 uy 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aHandbook of learning and approximate dynamic programming /$f[edited by] Jennie Si ... [et al.] 210 1$aHoboken, New Jersey :$cIEEE Press,$dc2004. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2004] 215 $a1 PDF (xxi, 644 pages) $cillustrations 225 1 $aIEEE press series on computational intelligence ;$v2 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$aPrint version: 9780471660545 320 $aIncludes bibliographical references and index. 327 $aForeword. -- 1. ADP: goals, opportunities and principles. -- Part I: Overview. -- 2. Reinforcement learning and its relationship to supervised learning. -- 3. Model-based adaptive critic designs. -- 4. Guidance in the use of adaptive critics for control. -- 5. Direct neural dynamic programming. -- 6. The linear programming approach to approximate dynamic programming. -- 7. Reinforcement learning in large, high-dimensional state spaces. -- 8. Hierarchical decision making. -- Part II: Technical advances. -- 9. Improved temporal difference methods with linear function approximation. -- 10. Approximate dynamic programming for high-dimensional resource allocation problems. -- 11. Hierarchical approaches to concurrency, multiagency, and partial observability. -- 12. Learning and optimization - from a system theoretic perspective. -- 13. Robust reinforcement learning using integral-quadratic constraints. -- 14. Supervised actor-critic reinforcement learning. -- 15. BPTT and DAC - a common framework for comparison. -- Part III: Applications. -- 16. Near-optimal control via reinforcement learning. -- 17. Multiobjective control problems by reinforcement learning. -- 18. Adaptive critic based neural network for control-constrained agile missile. -- 19. Applications of approximate dynamic programming in power systems control. -- 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. -- 21. Helicopter flight control using direct neural dynamic programming. -- 22. Toward dynamic stochastic optimal power flow. -- 23. Control, optimization, security, and self-healing of benchmark power systems. 330 $a. A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code. Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book. Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented. The contributors are leading researchers in the field. 410 0$aIEEE press series on computational intelligence ;$v2 606 $aDynamic programming 606 $aAutomatic programming (Computer science) 606 $aMachine learning 606 $aControl theory 606 $aSystems engineering 606 $aEngineering & Applied Sciences$2HILCC 606 $aCivil & Environmental Engineering$2HILCC 606 $aComputer Science$2HILCC 606 $aOperations Research$2HILCC 615 0$aDynamic programming 615 0$aAutomatic programming (Computer science) 615 0$aMachine learning 615 0$aControl theory 615 0$aSystems engineering 615 7$aEngineering & Applied Sciences 615 7$aCivil & Environmental Engineering 615 7$aComputer Science 615 7$aOperations Research 676 $a519.7/03 701 $aSi$b Jennie$0845885 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910133842403321 996 $aHandbook of learning and approximate dynamic programming$91888761 997 $aUNINA