Handbook of Markov Decision Processes-Methods and Applications [First pages of each chapter]

Handbook of Markov Decision Processes: M

📁 Handbook of Markov Decision Processes: Methods and Applications

✍ Eugene A. Feinberg, Adam Shwartz (auth.), Eugene A. Feinberg, Adam Shwartz (eds. 📂 Library 📅 2002 🏛 Springer US 🌐 English

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future resea

Handbook of Markov Decision Processes: M

📁 Handbook of Markov Decision Processes: Methods and Applications

✍ Eugene A. Feinberg, Adam Shwartz (eds.) 📂 Library 📅 2002,2012 🏛 Springer 🌐 English

Continuous-Time Markov Decision Processe

📁 Continuous-Time Markov Decision Processes: Theory and Applications

✍ Xianping Guo, Onésimo Hernández-Lerma (auth.) 📂 Library 📅 2009 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of po

Markov Decision Processes With Their App

📁 Markov Decision Processes With Their Applications

✍ Professor Qiying Hu, Professor Wuyi Yue (auth.) 📂 Library 📅 2008 🏛 Springer US 🌐 English

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time

Markov Decision Processes with Their App

📁 Markov Decision Processes with Their Applications

✍ Qiying Hu, Wuyi Yue 📂 Library 📅 2008 🏛 Springer US 🌐 English

Markov Decision Processes With Their App

📁 Markov Decision Processes With Their Applications

✍ Professor Qiying Hu, Professor Wuyi Yue (auth.) 📂 Library 📅 2008 🏛 Springer US 🌐 English

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time