A review of cooperative multi-agent deep reinforcement learning
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 …
Scalable multi-agent reinforcement learning for dynamic coordinated multipoint clustering
Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be
useful in the wireless communication area. However, for optimization problems in large …
useful in the wireless communication area. However, for optimization problems in large …
[HTML][HTML] A survey on multi-agent reinforcement learning and its application
Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …
presents a comprehensive survey of MARL and its applications. We trace the historical …
Cmix: Deep multi-agent reinforcement learning with peak and average constraints
In many real-world tasks, a team of learning agents must ensure that their optimized policies
collectively satisfy required peak and average constraints, while acting in a decentralized …
collectively satisfy required peak and average constraints, while acting in a decentralized …
Provably learning nash policies in constrained markov potential games
Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems
with multiple agents, where each agent optimizes its own objective. In many real-world …
with multiple agents, where each agent optimizes its own objective. In many real-world …
Provably efficient generalized lagrangian policy optimization for safe multi-agent reinforcement learning
We examine online safe multi-agent reinforcement learning using constrained Markov
games in which agents compete by maximizing their expected total rewards under a …
games in which agents compete by maximizing their expected total rewards under a …
Efficient resource allocation in multi-UAV assisted vehicular networks with security constraint and attention mechanism
With the rapid development of intelligent transportation systems, there is an increasingly
strong demand for low-latency and high-bandwidth vehicular services. Unmanned aerial …
strong demand for low-latency and high-bandwidth vehicular services. Unmanned aerial …
Attention actor-critic algorithm for multi-agent constrained co-operative reinforcement learning
In this work, we consider the problem of computing optimal actions for Reinforcement
Learning (RL) agents in a co-operative setting, where the objective is to optimize a common …
Learning (RL) agents in a co-operative setting, where the objective is to optimize a common …
Byzantine-robust federated deep deterministic policy gradient
Federated reinforcement learning (FRL) combines multi-agent reinforcement learning
(MARL) and federated learning (FL) so that multiple agents can exchange messages with a …
(MARL) and federated learning (FL) so that multiple agents can exchange messages with a …
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
Constrained cooperative multi-agent reinforcement learning (MARL) is an emerging
learning framework that has been widely applied to manage multi-agent systems, and many …
learning framework that has been widely applied to manage multi-agent systems, and many …