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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 MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple...

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 MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple...

Providing an introduction, the scientific background, case studies and future perspectives of in-situ arsenic remediation technologies for soils, soil water and groundwater at geogenic and anthropogenic contaminated sites. The case studies present in-situ technologies about natural arsenic, specifically arsenate and arsenite, but also about organic arsenic compounds. This work covers geochemical, microbiological and plant ecological solutions for arsenic remediation.

It will serve as a standard textbook for (post-)graduate students and researchers in the field of Environmental...