Uncertainties are widespread in the optimization of process systems, such as uncertainties
in process technologies, prices, and customer demands. In this paper, we review the basic …

The deep reinforcement learning (DRL) community has published remarkable results on
complex strategic planning problems, most famously in virtual scenarios for board and video …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

In many sequential decision-making problems one is interested in minimizing an expected
cumulative cost while taking into account risk, ie, increased awareness of events of small …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

We propose a general framework for entropy-regularized average-reward reinforcement
learning in Markov decision processes (MDPs). Our approach is based on extending the …

This is a substantial revision of the previous edition with added new material. The
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …

Endowing robots with the capability of assessing risk and making risk-aware decisions is
widely considered a key step toward ensuring safety for robots operating under uncertainty …

We study resource planning strategies, including the integrated healthcare resources'
allocation and sharing as well as patients' transfer, to improve the response of health …

In this paper we discuss statistical properties and convergence of the Stochastic Dual
Dynamic Programming (SDDP) method applied to multistage linear stochastic programming …