One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …

The cross-entropy (CE) method is a new generic approach to combinatorial and multi-
extremal optimization and rare event simulation. The purpose of this tutorial is to give a …

Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

Resource management problems in systems and networking often manifest as difficult
online decision making tasks where appropriate solutions depend on understanding the …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

This book is a comprehensive and accessible introduction to the cross-entropy (CE) method.
The CE method started life around 1997 when the first author proposed an adaptive …

Policy evaluation is an essential step in most reinforcement learning approaches. It yields a
value function, the quality assessment of states for a given policy, which can be used in a …

The cross-entropy method is a versatile heuristic tool for solving difficult estimation and
optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an …