Mining in proof-of-work blockchains has become an expensive affair requiring
specialized hardware capable of executing several megahashes per second at huge
electricity costs. Miners earn a reward each time they mine a block within the
longest chain, which helps offset their mining costs. It is therefore of
interest to miners to maximize the number of mined blocks in the blockchain and
increase revenue. A key factor affecting mining rewards earned is the
connectivity between miners in the peer-to-peer network. To maximize rewards a
miner must choose its network connections carefully, ensuring existence of
paths to other miners that are on average of a lower latency compared to paths
between other miners. We formulate the problem of deciding whom to connect to
for miners as a combinatorial bandit problem. Each node picks its neighbors
strategically to minimize the latency to reach 90\% of the hash power of the
network relative to the 90-th percentile latency from other nodes. A key
contribution of our work is the use of a network coordinates based model for
learning the network structure within the bandit algorithm. Experimentally we
show our proposed algorithm outperforming or matching baselines on diverse
network settings.

矿工在投入大量电力成本的情况下，在工作证明区块链中开采已成为一种昂贵的事务。为了最大化挖掘收益，矿工需要在节点之间选择网络连接，以减少延迟并提高挖矿奖励。本研究通过使用基于网络坐标的模型解决这一问题，并在实验中证明了该算法在不同网络设置下优于或与其他基准算法相当。

Cobalt: 优化 PoW 网络游戏中的挖矿奖励

Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

We consider a contextual combinatorial bandit problem where in each round a
learning agent selects a subset of arms and receives feedback on the selected
arms according to their scores. The score of an arm is an unknown function of
the arm's feature. Approximating this unknown score function with deep neural
networks, we propose algorithms: Combinatorial Neural UCB ($\texttt{CN-UCB}$)
and Combinatorial Neural Thompson Sampling ($\texttt{CN-TS}$). We prove that
$\texttt{CN-UCB}$ achieves $\tilde{\mathcal{O}}(\tilde{d} \sqrt{T})$ or
$\tilde{\mathcal{O}}(\sqrt{\tilde{d} T K})$ regret, where $\tilde{d}$ is the
effective dimension of a neural tangent kernel matrix, $K$ is the size of a
subset of arms, and $T$ is the time horizon. For $\texttt{CN-TS}$, we adapt an
optimistic sampling technique to ensure the optimism of the sampled
combinatorial action, achieving a worst-case (frequentist) regret of
$\tilde{\mathcal{O}}(\tilde{d} \sqrt{TK})$. To the best of our knowledge, these
are the first combinatorial neural bandit algorithms with regret performance
guarantees. In particular, $\texttt{CN-TS}$ is the first Thompson sampling
algorithm with the worst-case regret guarantees for the general contextual
combinatorial bandit problem. The numerical experiments demonstrate the
superior performances of our proposed algorithms.

本文提出了两个基于深度神经网络的组合赌博算法：CN-UCB 和 CN-TS，它们是首个在组合赌博问题中实现遗憾性能保证的算法。通过数值实验，证明了我们的算法有更好的性能。