Corpus ID: 202583612

Emergent Tool Use From Multi-Agent Autocurricula

@article{Baker2020EmergentTU,
  title={Emergent Tool Use From Multi-Agent Autocurricula},
  author={Bowen Baker and I. Kanitscheider and T. Markov and Yi Wu and Glenn Powell and Bob McGrew and Igor Mordatch},
  journal={ArXiv},
  year={2020},
  volume={abs/1909.07528}
}
  • Bowen Baker, I. Kanitscheider, +4 authors Igor Mordatch
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build… CONTINUE READING
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