# Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.04696 · HTML: https://mlsanity.com/paper/2209.04696/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [The Optimal Reward Baseline for Gradient-Based Reinforcement Learning](https://mlsanity.com/paper/1301.2315/) (2013)
- [Evolution-Guided Policy Gradient in Reinforcement Learning](https://mlsanity.com/paper/1805.07917/) (2018)
- [Collaborative Evolutionary Reinforcement Learning](https://mlsanity.com/paper/1905.00976/) (2019)
- [Mean Field Games Flock! The Reinforcement Learning Way](https://mlsanity.com/paper/2105.07933/) (2021)
- [Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination](https://mlsanity.com/paper/1906.07315/) (2019)
- [Convergence of some Mean Field Games systems to aggregation and flocking models](https://mlsanity.com/paper/2004.04403/) (2020)
- [A mean-field game model for homogeneous flocking](https://mlsanity.com/paper/1803.05250/) (2018)
- [Semi-linear Poisson-mediated Flocking in a Cucker-Smale Model](https://mlsanity.com/paper/2102.08772/) (2021)
- [Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning](https://mlsanity.com/paper/2101.08074/) (2021)

## Built on by (most cited first, 3 of 3)

- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process](https://mlsanity.com/paper/2506.01080/) (2025)
- [Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat](https://mlsanity.com/paper/2605.25091/) (2026)

## Abstract (arXiv)

Flocking is a very challenging problem in a multi-agent system; traditional flocking methods also require complete knowledge of the environment and a precise model for control. In this paper, we propose Evolutionary Multi-Agent Reinforcement Learning (EMARL) in flocking tasks, a hybrid algorithm that combines cooperation and competition with little prior knowledge. As for cooperation, we design the agents' reward for flocking tasks according to the boids model. While for competition, agents with high fitness are designed as senior agents, and those with low fitness are designed as junior, letting junior agents inherit the parameters of senior agents stochastically. To intensify competition, we also design an evolutionary selection mechanism that shows effectiveness on credit assignment in flocking tasks. Experimental results in a range of challenging and self-contrast benchmarks demonstrate that EMARL significantly outperforms the full competition or cooperation methods.