Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
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 …
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …
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 …
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 …
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and …