Learnable Randomization as Commitment Against Adaptive Optimizers
This work studies unpredictable near-optimal policies (UNOP), which mix uniformly on near-best actions that remain individually rational and which can be learned and the optimizer can match its best response, while the user's payoff stays higher because the mixture changes which action is targeted.
Zi-Han Deng, Chuanzhi Xu, Xiao-Zhen Zhong et al.
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