Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
The paper presents a new framework for multi-agent reinforcement learning in cooperative air combat scenarios. It introduces the Adversarial Curriculum and Evolutionary-enhanced Multi-agent Proximal Policy Optimization (ACE-MAPPO) to address challenges faced by unmanned combat aerial vehicles. Experimental results indicate that ACE-MAPPO outperforms existing methods in training stability and convergence speed.
- ▪The proposed ACE-MAPPO framework integrates evolutionary algorithms with multi-agent reinforcement learning.
- ▪A genetic soft update mechanism is introduced to enhance population diversity.
- ▪The method demonstrates improved training stability and win rates in multi-aircraft cooperative scenarios.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25091 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.25091 (cs) [Submitted on 24 May 2026] Title:Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat Authors:Chengwei Li, Junlin Liu, Yang Gao View a PDF of the paper titled Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat, by Chengwei Li and 2 other authors View PDF HTML (experimental) Abstract:As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aerial vehicles (UCAVs) faces significant challenges due to high-dimensional state spaces, discrete action commands, and strongly adversarial dynamic environments.
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