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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $13K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

1-2x

3yr ROI

10-25x

Automation tools have long sales cycles but high retention. Expect $5K MRR by 6mo, accelerating to $500K+ ARR at 3yr as enterprises adopt.

Talent Scout

L

Longbo Huang

Tsinghua University

Z

Zhuoran Li

Tsinghua University

H

Hai Zhong

Tsinghua University

X

Xun Wang

Tsinghua University

Find Similar Experts

Multi-Agent experts on LinkedIn & GitHub

Founder's Pitch

"Develop diffusion-based multi-agent coordination tools to optimize online reinforcement learning tasks."

Multi-Agent CoordinationScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

This research advances the capabilities of multi-agent systems to efficiently coordinate actions in complex environments, a crucial feature for applications like autonomous driving and robotics.

Product Angle

The product could be developed as a software tool that integrates with existing fleet management systems to optimize route planning and reduce energy consumption by coordinating vehicle behaviors.

Disruption

It could replace traditional MARL systems that rely on simpler policy models which lack the ability to explore and exploit the full range of possible agent interactions.

Product Opportunity

The market for efficient multi-agent coordination is substantial, especially in sectors like autonomous vehicles, warehouse automation, and drone fleet management, where improved coordination can lead to significant cost savings and performance enhancements.

Use Case Idea

A commercial tool for managing and optimizing traffic flow in autonomous vehicle fleets using the OMAD approach for agent coordination.

Science

The paper introduces a new framework, OMAD, which uses diffusion policies within the centralised training with decentralised execution paradigm to improve multi-agent coordination. It replaces traditional unimodal policy distributions with multimodal diffusion models to enhance expressiveness and solve the intractability of policy likelihoods using a new tractable entropy objective.

Method & Eval

Tested on MPE and MAMuJoCo benchmarks, the OMAD framework demonstrated up to a 5x improvement in sample efficiency compared to state-of-the-art methods, validating its effectiveness in diverse multi-agent scenarios.

Caveats

The framework may face challenges in scenarios requiring extremely rapid decision-making due to the complexity of modelling diffusion processes, and implementation could be hindered by the intricacies of tuning entropy-related parameters.

Author Intelligence

Longbo Huang

LEAD
Tsinghua University

Zhuoran Li

Tsinghua University

Hai Zhong

Tsinghua University

Xun Wang

Tsinghua University

Qingxin Xia

ByteDance

Lihua Zhang

ByteDance

References (70)

[1]
TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times
2025Jintao Zhang, Kaiwen Zheng et al.
[2]
Multi-Agent Reinforcement Learning with Communication-Constrained Priors
2025Guang Yang, Tianpei Yang et al.
[3]
Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
2025Ziqi Wang, Jiashun Liu et al.
[4]
Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control
2025Cheng Hua, Jiawen Gu et al.
[5]
Enhanced DACER Algorithm with High Diffusion Efficiency
2025Yinuo Wang, Mining Tan et al.
[6]
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
2025Yang Zhang, Xinran Li et al.
[7]
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps
2025Ningyuan Yang, Jiaxuan Gao et al.
[8]
Multi-Agent Systems for Robotic Autonomy with LLMs
2025Junhong Chen, Ziqi Yang et al.
[9]
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2025A. Kontogiannis, Konstantinos Papathanasiou et al.
[10]
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2025Amber Xie, Oleh Rybkin et al.
[11]
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2025Xiaoyi Dong, Jian Cheng et al.
[12]
DIME:Diffusion-Based Maximum Entropy Reinforcement Learning
2025Onur Celik, Zechu Li et al.
[13]
Efficient Online Reinforcement Learning for Diffusion Policy
2025Haitong Ma, Tianyi Chen et al.
[14]
Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning
2025Whiyoung Jung, Sunghoon Hong et al.
[15]
Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport
2025Lvmin Zhang, A. Rao et al.
[16]
INS: Interaction-aware Synthesis to Enhance Offline Multi-agent Reinforcement Learning
2025Yuqian Fu, Yuanheng Zhu et al.
[17]
Revisiting Cooperative Off-Policy Multi-Agent Reinforcement Learning
2025Yueheng Li, Guangming Xie et al.
[18]
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
2024Yangkun Chen, Kai Yang et al.
[19]
WorldSimBench: Towards Video Generation Models as World Simulators
2024Yiran Qin, Zhelun Shi et al.
[20]
Diffusion Policy Policy Optimization
2024Allen Z. Ren, Justin Lidard et al.

Showing 20 of 70 references