Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems

2020-11-30 · Niko A. Grupen, Daniel D. Lee, Bart Selman

In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to high-bandwidth (e.g. compositional language) communication that is based on the cognitive, perceptual, and behavioral capabilities of social agents. Through a series of experiments with pursuit-evasion games, we identify multi-agent reinforcement learning algorithms as a computational model for the low-bandwidth end of the communication spectrum.

📄 PDF Abstract BibTeX arXiv:2011.14890

Code (0)

등록된 구현이 없습니다.

Tasks

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

Preference Communication in Multi-Objective Normal-Form Games

2021-11-17 · Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Rădulescu

We consider preference communication in two-player multi-objective normal-form games. In such games, the payoffs resulting from joint actions are vector-valued. Taking a utility-based approach, we assume there exists a u…

Form

Emergent Communication in a Multi-Modal, Multi-Step Referential Game

2017-05-29 · ICLR 2018 1 · Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho

Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and th…

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

2019-11-16 · ICML 2020 1 · Rundong Wang, Xu He, Runsheng Yu, Wei Qiu 외

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a scheduler. The protocol and scheduler jo…

Multi-agent Reinforcement Learning Reinforcement Learning Scheduling

Learning Agent Communication under Limited Bandwidth by Message Pruning

2019-12-03 · Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong 외

Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy fo…

Deep Reinforcement Learning Reinforcement Learning

Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization

2026-02-02 · Ahmad Farooq, Kamran Iqbal arxiv

Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significantly impact coordination effectiveness. We present a framework that combines inf…

Multi-agent Reinforcement Learning