Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning
This addresses flocking control for multi-agent systems, offering an incremental improvement over existing methods.
The paper tackled flocking in multi-agent systems by proposing Evolutionary Multi-Agent Reinforcement Learning (EMARL), a hybrid algorithm combining cooperation and competition with minimal prior knowledge, and demonstrated that EMARL significantly outperforms full competition or cooperation methods in benchmarks.
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.