English, Article, Journal or magazine article edition: On the Convergence of Reinforcement Learning Alan Beggs

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Bookmark: http://trove.nla.gov.au/version/20499
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • On the Convergence of Reinforcement Learning
Author
  • Alan Beggs
Physical Description
  • preprint
Notes
  • This paper examines the convergence of payoffs and strategies in Erev and Roth`s model of reinforcement learning. When all players use this rule it eliminates iteratively dominated strategies and in two-person constant-sum games average payoffs converge to the value of the game. Strategies converge in constant-sum games with unique equilibria if they are pure or in 2 × 2 games also if they are mixed. The long-run behaviour of the learning rule is governed by equations related to Maynard Smith`s version of the replicator dynamic. Properties of the learning rule against general opponents are also studied. In particular it is shown that it guarantees that the lim sup of a player`s average payoffs is at least his minmax payoff.
  • reinforcement learning, games
  • RePEc:oxf:wpaper:096
Language
  • English
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