Corpus ID: 203592142

A Generalized Training Approach for Multiagent Learning

@article{Muller2020AGT,
  title={A Generalized Training Approach for Multiagent Learning},
  author={P. Muller and Shayegan Omidshafiei and M. Rowland and K. Tuyls and Julien P{\'e}rolat and S. Liu and D. Hennes and Luke Marris and Marc Lanctot and Edward Hughes and Z. Wang and G. Lever and N. Heess and T. Graepel and R{\'e}mi Munos},
  journal={ArXiv},
  year={2020},
  volume={abs/1909.12823}
}
  • P. Muller, Shayegan Omidshafiei, +12 authors Rémi Munos
  • Published 2020
  • Computer Science
  • ArXiv
  • This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1) encompasses well-known algorithms such as fictitious play and double oracle as special cases, and (2) in principle applies to general-sum, many-player games. Despite this, prior studies of PSRO have been focused on two-player zero-sum games, a regime wherein Nash equilibria are tractably computable. In moving from… CONTINUE READING

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