Ai alignment: A comprehensive survey
AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
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] 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 …
Cooperative inverse reinforcement learning
D Hadfield-Menell, SJ Russell… - Advances in neural …, 2016 - proceedings.neurips.cc
For an autonomous system to be helpful to humans and to pose no unwarranted risks, it
needs to align its values with those of the humans in its environment in such a way that its …
needs to align its values with those of the humans in its environment in such a way that its …
Combining deep reinforcement learning and search for imperfect-information games
The combination of deep reinforcement learning and search at both training and test time is
a powerful paradigm that has led to a number of successes in single-agent settings and …
a powerful paradigm that has led to a number of successes in single-agent settings and …
On improving model-free algorithms for decentralized multi-agent reinforcement learning
Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential
sample complexity dependence on the number of agents, a phenomenon known as the …
sample complexity dependence on the number of agents, a phenomenon known as the …
System level synthesis
This article surveys the System Level Synthesis framework, which presents a novel
perspective on constrained robust and optimal controller synthesis for linear systems. We …
perspective on constrained robust and optimal controller synthesis for linear systems. We …
Bayesian action decoder for deep multi-agent reinforcement learning
When observing the actions of others, humans make inferences about why they acted as
they did, and what this implies about the world; humans also use the fact that their actions …
they did, and what this implies about the world; humans also use the fact that their actions …
Provably efficient reinforcement learning in decentralized general-sum markov games
This paper addresses the problem of learning an equilibrium efficiently in general-sum
Markov games through decentralized multi-agent reinforcement learning. Given the …
Markov games through decentralized multi-agent reinforcement learning. Given the …