Planning with theory of mind
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 …
traditional answer is that Theory of Mind is used for predicting others' thoughts and actions …
An overview of multi-agent reinforcement learning from game theoretical perspective
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 …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
[图书][B] Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions: by Warren B. Powell (ed.), Wiley (2022). Hardback. ISBN …
I Halperin - 2022 - Taylor & Francis
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 …
optimization? And finally, can it be used for applications to quantitative finance for my current …
[图书][B] A concise introduction to decentralized POMDPs
FA Oliehoek, C Amato - 2016 - Springer
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 …
cooperative systems. It is aimed at graduate students and researchers in the fields of …
Learning to drive from a world on rails
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 …
model-based approach. A forward model of the world supervises a driving policy that …
A survey of multi-objective sequential decision-making
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 …
pose unique challenges for research in decision-theoretic planning and learning, which has …
Statistical relational artificial intelligence: Logic, probability, and computation
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 …
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …
[图书][B] Probabilistic graphical models: principles and techniques
D Koller, N Friedman - 2009 - books.google.com
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 …
would enable a computer to use available information for making decisions. Most tasks …
[HTML][HTML] Deliberation for autonomous robots: A survey
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 …
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …
On the convergence of projective-simulation–based reinforcement learning in Markov decision processes
WL Boyajian, J Clausen, LM Trenkwalder… - Quantum machine …, 2020 - Springer
In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …
tasks has significantly increased. Many algorithms speeding up supervised and …