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Related Experiment Video

Updated: Sep 26, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Deep reinforcement learning with emergent communication for coalitional negotiation games.

Siqi Chen¹, Yang Yang¹, Ran Su¹

¹College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China.

Mathematical Biosciences and Engineering : MBE

|April 18, 2022

Summary

Autonomous agents using deep reinforcement learning (DRL) can negotiate complex coalitional games. DALSL agents form teams, fairly distribute gains, and use emergent communication to improve outcomes.

Keywords:

cooperative games deep learning emergent communication multi-agent systems reinforcement learning

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Area of Science:

Artificial Intelligence
Game Theory
Multi-Agent Systems

Background:

Cooperation is essential for complex tasks intractable for single agents.
Coalitional games involve forming groups to produce jointly and share surpluses.
Strategic negotiation for fair gain allocation is challenging among independent, selfish agents.

Purpose of the Study:

To develop an autonomous agent capable of handling arbitrary coalitional games without human input.
To enable agents to negotiate and allocate gains strategically.
To investigate the role of emergent communication in multi-agent negotiation.

Main Methods:

Employed deep reinforcement learning (DRL) to create the DALSL agent.
Designed agents capable of emergent communication for information exchange.
Tested agent performance in various coalitional game scenarios.

Main Results:

The DALSL agent successfully formed teams and distributed benefits fairly.
Emergent communication facilitated information exchange, promoting smaller coalitions and faster negotiations.
DALSL agents achieved higher payoffs compared to handcrafted and other RL-based agents, especially when communication was enabled.

Conclusions:

DRL-powered autonomous agents can effectively navigate complex coalitional negotiation games.
Emergent communication significantly enhances negotiation efficiency and outcomes in multi-agent systems.
The DALSL agent demonstrates a robust approach to strategic cooperation and fair resource allocation.