Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Abstract Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be
promising for many real-world applications. Previous works mainly focus on improving …
promising for many real-world applications. Previous works mainly focus on improving …
Vast: Value function factorization with variable agent sub-teams
Value function factorization (VFF) is a popular approach to cooperative multi-agent
reinforcement learning in order to learn local value functions from global rewards. However …
reinforcement learning in order to learn local value functions from global rewards. However …
Certifiably robust policy learning against adversarial multi-agent communication
Communication is important in many multi-agent reinforcement learning (MARL) problems
for agents to share information and make good decisions. However, when deploying trained …
for agents to share information and make good decisions. However, when deploying trained …
Safe multi-agent reinforcement learning for wireless applications against adversarial communications
Based on the network observations and learning parameters shared by the neighboring
learning agents, multi-agent reinforcement learning (RL) has to enhance the performance …
learning agents, multi-agent reinforcement learning (RL) has to enhance the performance …
[PDF][PDF] Towards anomaly detection in reinforcement learning
Identifying datapoints that substantially differ from normality is the task of anomaly detection
(AD). While AD has gained widespread attention in rich data domains such as images …
(AD). While AD has gained widespread attention in rich data domains such as images …
Certifiably robust policy learning against adversarial communication in multi-agent systems
Communication is important in many multi-agent reinforcement learning (MARL) problems
for agents to share information and make good decisions. However, when deploying trained …
for agents to share information and make good decisions. However, when deploying trained …
Byzantine robust cooperative multi-agent reinforcement learning as a bayesian game
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-
MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions …
MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions …
T3OMVP: A Transformer-Based Time and Team Reinforcement Learning Scheme for Observation-Constrained Multi-Vehicle Pursuit in Urban Area
Z Yuan, T Wu, Q Wang, Y Yang, L Li, L Zhang - Electronics, 2022 - mdpi.com
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to
vehicle decision-making in the Intelligent Transportation System (ITS). Multi-vehicle pursuit …
vehicle decision-making in the Intelligent Transportation System (ITS). Multi-vehicle pursuit …