Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
When to switch: planning and learning for partially observable multi-agent pathfinding
Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting
paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where …
paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where …
Multiagent trust region policy optimization
We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement
learning (MARL) for partially observable Markov games (POMGs). We show that the policy …
learning (MARL) for partially observable Markov games (POMGs). We show that the policy …
A Multiagent Cooperative Learning System With Evolution of Social Roles
Recent developments in reinforcement learning (RL) have been able to derive optimal
policies for sophisticated and capable agents, and shown to achieve human-level …
policies for sophisticated and capable agents, and shown to achieve human-level …
Data-driven time-varying formation-containment control for a heterogeneous air-ground vehicle team subject to active leaders and switching topologies
The optimal formation-containment control problem for a team of heterogeneous unmanned
air-ground vehicles (UA-GVs), subject to active leaders and switching topologies, is …
air-ground vehicles (UA-GVs), subject to active leaders and switching topologies, is …
Multi-UAV Roundup Inspired by Hierarchical Cognition Consistency Learning Based on an Interaction Mechanism
L Jiang, R Wei, D Wang - Drones, 2023 - mdpi.com
This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical
cognition consistency learning based on an interaction mechanism. First, a dynamic …
cognition consistency learning based on an interaction mechanism. First, a dynamic …
Learning multi-agent cooperation via considering actions of teammates
Recently value-based centralized training with decentralized execution (CTDE) multi-agent
reinforcement learning (MARL) methods have achieved excellent performance in …
reinforcement learning (MARL) methods have achieved excellent performance in …
SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning
It is challenging to train an efficient learning procedure with multiagent reinforcement
learning (MARL) when the number of agents increases as the observation space …
learning (MARL) when the number of agents increases as the observation space …
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Communication in multi-agent reinforcement learning (MARL) has been proven to effectively
promote cooperation among agents recently. Since communication in real-world scenarios …
promote cooperation among agents recently. Since communication in real-world scenarios …
Cooperative multi-agent transfer learning with coalition pattern decomposition
Knowledge transfer in cooperative multiagent reinforcement learning (MARL) has drawn
increasing attention in recent years. Unlike generalizing policies in single-agent tasks, it is …
increasing attention in recent years. Unlike generalizing policies in single-agent tasks, it is …