On the asymptotic optimality of the comb strategy for prediction with expert advice
Erhan Bayraktar, Ibrahim Ekren, Yili Zhang

TL;DR
This paper analyzes the asymptotic behavior of the comb strategy in prediction with expert advice, proving its optimality for four experts in an adversarial setting with geometric stopping.
Contribution
It provides the exact leading order expansion of the value function and confirms the asymptotic optimality of comb strategies for four experts.
Findings
Exact leading order expansion of the value function computed
Proved asymptotic optimality of comb strategies for four experts
Supports conjecture by Gravin et al. on strategy optimality
Abstract
For the problem of prediction with expert advice in the adversarial setting with geometric stopping, we compute the exact leading order expansion for the long time behavior of the value function. Then, we use this expansion to prove that as conjectured in Gravin et al. [12], the comb strategies are indeed asymptotically optimal for the adversary in the case of 4 experts.
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Machine Learning and Algorithms · Reinforcement Learning in Robotics
