Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

2020-09-28 · Douglas De Rizzo Meneghetti, Reinaldo Augusto da Costa Bianchi

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

📄 PDF Abstract BibTeX arXiv:2009.13161

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information

2020-12-14 · Douglas De Rizzo Meneghetti, Reinaldo Augusto da Costa Bianchi

Inspired by recent advances in agent communication with graph neural networks, this work proposes the representation of multi-agent communication capabilities as a directed labeled heterogeneous agent graph, in which nod…

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning

2026-02-18 · Emile Anand, Richard Hoffmann, Sarah Liaw, Adam Wierman arxiv

Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales exponentially with the number of agents. Mea…

Multi-agent Reinforcement Learning

Heterogeneous Multi-Robot Reinforcement Learning

2023-01-17 · Matteo Bettini, Ajay Shankar, Amanda Prorok

Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) frameworks lack the ability to explicitl…

Graph Neural Network Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control

2023-10-17 · Xianyue Peng, Hang Gao, Shenyang Chen, Hao Wang 외

Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way. However, jointly controlling both in rea…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Traffic Signal Control

GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs

2026-04-26 · Tao Feng, Haozhen Zhang, Zijie Lei, Peixuan Han 외 arxiv

LLM routing has achieved promising results in integrating the strengths of diverse models while balancing efficiency and performance. However, to support more realistic and challenging applications, routing must extend i…

Computational Efficiency Reinforcement Learning