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 …
Reinforcement learning in robotic applications: a comprehensive survey
In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …
control systems. Still, researchers are trying to make a completely autonomous system that …
A theoretical analysis of deep Q-learning
Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …
foundation is less well understood. In this work, we make the first attempt to theoretically …
Mean field multi-agent reinforcement learning
Existing multi-agent reinforcement learning methods are limited typically to a small number
of agents. When the agent number increases largely, the learning becomes intractable due …
of agents. When the agent number increases largely, the learning becomes intractable due …
Reinforcement learning-based physical cross-layer security and privacy in 6G
Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
Deep reinforcement learning from self-play in imperfect-information games
J Heinrich, D Silver - arXiv preprint arXiv:1603.01121, 2016 - arxiv.org
Many real-world applications can be described as large-scale games of imperfect
information. To deal with these challenging domains, prior work has focused on computing …
information. To deal with these challenging domains, prior work has focused on computing …
Intelligent reflecting surface assisted anti-jamming communications: A fast reinforcement learning approach
Malicious jamming launched by smart jammers can attack legitimate transmissions, which
has been regarded as one of the critical security challenges in wireless communications …
has been regarded as one of the critical security challenges in wireless communications …
UAV relay in VANETs against smart jamming with reinforcement learning
Frequency hopping-based antijamming techniques are not always applicable in vehicular
ad hoc networks (VANETs) due to the high mobility of onboard units (OBUs) and the large …
ad hoc networks (VANETs) due to the high mobility of onboard units (OBUs) and the large …
Dealing with non-stationarity in multi-agent deep reinforcement learning
Recent developments in deep reinforcement learning are concerned with creating decision-
making agents which can perform well in various complex domains. A particular approach …
making agents which can perform well in various complex domains. A particular approach …