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We propose a learning dynamic with agents using samples of past play to estimate the distribution of other players' strategy choices and best responding to this estimate. To account for noisy play, estimated distributions over other players' strategy choices have full support in the other...
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This paper describes the “Bounded Memory, Inertia, Sampling and Weighting” (BI-SAW) model, which won the http …
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We consider a model of evolution in games in which a revising agent observes the actions of a random number of randomly sampled opponents and then chooses a best response to the distribution of actions in the sample. We provide a condition on the distribution of sample sizes under which an...
Persistent link: https://www.econbiz.de/10011673541
We consider a model of evolution in games in which a revising agent observes the actions of a random number of randomly sampled opponents and then chooses a best response to the distribution of actions in the sample. We provide a condition on the distribution of sample sizes under which an...
Persistent link: https://www.econbiz.de/10014174095
dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training …/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training … error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement …
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