Understanding Theory of Mind should begin with an analysis of the problems it solves. The
traditional answer is that Theory of Mind is used for predicting others' thoughts and actions …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

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 …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

We learn an interactive vision-based driving policy from pre-recorded driving logs via a
model-based approach. A forward model of the world supervises a driving policy that …

Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …

Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

A complete and accessible introduction to the real-world applications of approximate
dynamic programming With the growing levels of sophistication in modern-day operations, it …