Abstract
Abstract Swarm robots are a branch of robotics that draws inspiration from biological swarms to mimic their collective behavior. Automatic design methods are part of swarm engineering, depend on artificial intelligence algorithms to produce the collective behavior of robots. In general, they follow two-approach evolutionary algorithms like practical swarm optimization and reinforcement learning. This paper studies these approaches, illustrating the effect of modifications and enhancements of algorithms for both directions, showing important parameters considered for the best performance of the swarm, and explaining the methods and advantages of using deep learning to reinforcement learning.
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