  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.
