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 …
Multi-agent deep reinforcement learning: a survey
S Gronauer, K Diepold - Artificial Intelligence Review, 2022 - Springer
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
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 …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
A distributional code for value in dopamine-based reinforcement learning
Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …
of empirical phenomena, providing a unifying framework for understanding the …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
A survey and critique of multiagent deep reinforcement learning
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
Multi-agent systems: A survey
Multi-agent systems (MASs) have received tremendous attention from scholars in different
disciplines, including computer science and civil engineering, as a means to solve complex …
disciplines, including computer science and civil engineering, as a means to solve complex …
Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …