Exploration by Optimisation in Partial Monitoring

Tor Lattimore; Csaba Szespvári

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Exploration by Optimisation in Partial Monitoring

机译：部分监测优化探讨

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We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 6m k^(3/2) sqrt(n log(k)), where m is the number of signals. This matches the best known information-theoretic upper bound derived via Bayesian minimax duality. The same algorithm also achieves near-optimal regret for full information, bandit and globally observable games. High probability bounds and simple experiments are also provided.

机译：我们提供一种新的普通k-action D-结果部分监测算法，适应性，直观和有效。突出显示是，对于非退化的本地观察游戏，N级MIMIMAX遗憾受到6M k ^（3/2）SQRT（n log（k）），其中m是信号的数量。这与通过贝叶斯Minimax二元性导出的最佳已知的信息 - 理论上界。同样的算法还实现了全面信息，强盗和全球可观察的游戏的近乎最佳的遗憾。还提供了高概率界限和简单的实验。

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《JMLR: Workshop and Conference Proceedings》 |2020年第2010期|共28页
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Tor Lattimore; Csaba Szespvári;
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Exploration by Optimisation in Partial Monitoring

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