We describe a model-free approach to bidding in the Ad-Auctions Trading Agents Competition: First, a simple and robust yet high-performing agent using a Regret Minimization optimization algorithm for the 2010 competition, followed by our top performing agent for the 2011 competition, still using simplified modeling and optimization methods. Specifically, we model the user populations using particle filters, but base the observations on a Nearest Neighbor estimator (instead of game specific parameters). We implement a simple and effective bid optimization algorithm by applying the equimarginal principle combined with perplexity-based regularization. The implementation of our 2011 agent also remains model-free in the sense that we do not attempt to model the competing agents behavior for estimating costs and associated game parameters.


User Population Trading Agent Regularization Factor Reserve Prex Neighbor Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mariano Schain
    • 1
  • Shai Hertz
    • 1
  • Yishay Mansour
    • 1
  1. 1.Tel Aviv UniversityIsrael

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