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Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
Disaster management and emerging technologies: a performance-based perspective
Purpose This paper aims to analyse how emerging technologies (ETs) impact on improving
performance in disaster management (DM) processes and, concretely, their impact on the …
performance in disaster management (DM) processes and, concretely, their impact on the …
A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning
X Ni, W Hu, Q Fan, Y Cui, C Qi - Expert Systems with Applications, 2024 - Elsevier
Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities
among various meta-heuristic algorithms. However, its monotonous and one-dimensional …
among various meta-heuristic algorithms. However, its monotonous and one-dimensional …
Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence
Purpose The global health crisis represents an unprecedented opportunity for the
development of artificial intelligence (AI) solutions. This paper aims to integrate explainable …
development of artificial intelligence (AI) solutions. This paper aims to integrate explainable …
A bi-level network-wide cooperative driving approach including deep reinforcement learning-based routing
Cooperative driving of connected and automated vehicles (CAVs) has attracted extensive
attention and researchers have proposed various approaches. However, existing …
attention and researchers have proposed various approaches. However, existing …
Credit assignment in heterogeneous multi-agent reinforcement learning for fully cooperative tasks
Credit assignment poses a significant challenge in heterogeneous multi-agent
reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL …
reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL …
A reinforcement learning-based strategy updating model for the cooperative evolution
The emergence of cooperation between competing agents has been commonly studied
through evolutionary games, but such cooperation often requires a mechanism or a third …
through evolutionary games, but such cooperation often requires a mechanism or a third …
[HTML][HTML] Applying multi-agent deep reinforcement learning for contention window optimization to enhance wireless network performance
This paper investigates the Contention Window (CW) optimization problem in multi-agent
scenarios, where the fully cooperative among mobile stations is considered. A partially …
scenarios, where the fully cooperative among mobile stations is considered. A partially …
Routing algorithms as tools for integrating social distancing with emergency evacuation
One of the lessons from the COVID-19 pandemic is the importance of social distancing, even
in challenging circumstances such as pre-hurricane evacuation. To explore the implications …
in challenging circumstances such as pre-hurricane evacuation. To explore the implications …
POMCP-based decentralized spatial task allocation algorithms for partially observable environments
Spatial task allocation has many applications in realistic multi-robot systems and has been
studied for several years by many researchers. However, most of the researches conducted …
studied for several years by many researchers. However, most of the researches conducted …