Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

最新推荐文章于 2025-12-16 10:44:31 发布

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#Multi-agents RL #强化学习 #multi-agent #python #机器学习

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本文探讨了在复杂多智能体环境中，如何通过CDS框架解决参数共享带来的行为相似性问题，强调了在保持学习效率的同时促进个体多样性的关键。研究背景涉及Dec-POMDP和CTDE，方法论结合了信息论目标与行动价值学习。实验部分展示了在GRF和SMAC等任务上的先进性能，对比了CDS与其他MARL算法如QPLEX和QMIX。

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

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 Motivation

Significance:
MARL is useful for many real-world applications, sensor networks, traffic management, coordinate robots, etc.
Problems:
Hard to learn effective policies in complex multi-agent scenarios, and action-obs space grows largely with number of agents. And PDSP (policy decentralization with shared params) is used to solve scalability problem.

But for PSDP, the drawback is: tasks usually require diversified policies among agents, while shared params lead to similar behaviors(under similar obs).

A tradeoff: sharing necessary params to accelerate learning while improve diversity.

Keywords:
MARL, PDSP, tradeoff

 Backgrounds:
Dec-POMDP(Decentralized partially observable MDP), CTDE, IGM,

 Model

Structure:
diversity-driven MARL framework
Theory:
Maximation of information-theoretic objective
Action-Value Learning for Balancing Diversity and Sharing
Overall Learning Objective

 Experiment

Metrics:
Benchmark tasks & Baselines:
Google Research Football(GRF), StarCraft II micro-management(SMAC)
CDS(proposed), QPLEX, QMIX, MAVEN, EOI
Design:
Demonstration of how the approach works.
Conclusion:
State-of-art result.

 Thinking

Pros:
 A novel mechanism of being diverse when necessary into shared multi-agent reinforcement learning
 The balance between individual diversity and group coordination
Cros:
No ablation studies shown and more explanation about L1 not shown

Links:
Video: https://sites.google.com/view/celebrate-diversity-shared
Code: https://github.com/lich14/CDS