Slither.io is a massively multiplayer online game in which up to 500 players control worm-like avatars and consume food to grow with the goal of becoming the largest player while avoiding running into one another. The platform serves as a good testbed for developing AI controlled agents due to its accessibility, mechanical simplicity, and unpredictability. In this paper, we develop a Slither.io bot using neuroevolution of augmenting topologies (NEAT) and compare its performance to that of the best open source bot available online (a high-performing expert system bot). With a fitness function based on the final size of the agent, our results show steady improvement in average score. We discuss the unique emergent behaviors observed by our top performing agents.
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Evolving unsupervised neural networks for Slither.io
Semantic Scholar · Computer Science · 2019
Abstract
Slither.io is a massively multiplayer online game in which up to 500 players control worm-like avatars and consume food to grow with the goal of becoming the largest player while avoiding running into one another. The platform serves as a good testbed for developing AI controlled agents due to its accessibility, mechanical simplicity, and unpredictability. In this paper, we develop a Slither.io bot using neuroevolution of augmenting topologies (NEAT) and compare its performance to that of the best open source bot available online (a high-performing expert system bot). With a fitness function based on the final size of the agent, our results show steady improvement in average score. We discuss the unique emergent behaviors observed by our top performing agents.