Abstract
A calibrated strategy can be obtained by performing a strategy that has no internal regret in some auxiliary game. Such a strategy can be constructed explicitly with the use of Blackwell’s approachability theorem, in an other auxiliary game. We establish the converse: a strategy that approaches a convex B-set can be derived from the construction of a calibrated strategy.
We develop these tools in the framework of a game with partial monitoring, where players do not observe the actions of their opponents but receive random signals, to define a notion of internal regret and construct strategies that have no such regret.
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Perchet, V. (2009). Calibration and Internal No-Regret with Random Signals. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2009. Lecture Notes in Computer Science(), vol 5809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04414-4_10
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DOI: https://doi.org/10.1007/978-3-642-04414-4_10
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