利用相邻任务相似性，跨任务迁移奖励样本以降低累计损失。
Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samples
- 基于UCB框架，从前序任务迁移奖励样本
- 理论证明迁移可减少整体累计损失
- 适合相似任务序列的在线决策场景
我们研究一个顺序多任务问题，每个任务建模为具有K个臂的随机多臂赌博机。假设这些赌博机任务在相邻意义上相似，即任意两个连续任务中各臂的均值奖励差异被一个参数所限制。我们提出了两种算法（一种假设该参数已知，另一种则未知），基于UCB方法，将前序任务的奖励样本迁移到后续任务中，以改善所有任务的总体遗憾。分析表明，与不进行迁移的情况相比，样本迁移能有效降低遗憾。我们在实验中验证了算法性能，结果表明其优于无迁移的标准UCB算法和一种简单的迁移算法。
原文摘要 · Abstract (English)
We consider a sequential multi-task problem, where each task is modeled as the stochastic multi-armed bandit with K arms. We assume the bandit tasks are adjacently similar in the sense that the difference between the mean rewards of the arms for any two consecutive tasks is bounded by a parameter. We propose two algorithms (one assumes the parameter is known while the other does not) based on UCB to transfer reward samples from preceding tasks to improve the overall regret across all tasks. Our analysis shows that transferring samples reduces the regret as compared to the case of no transfer. We provide empirical results for our algorithms, which show performance improvement over the standard UCB algorithm without transfer and a naive transfer algorithm.
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