图上最大覆盖问题的深度强化学习方法基准研究
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs
AI总结：
本文对五种近期的深度强化学习方法在最大覆盖问题（MCP）和影响力最大化（IM）上的表现进行了基准研究，发现传统理论算法在多数场景下优于深度强化学习方法，并指出了当前研究的潜在挑战。
摘要：
近年来，采用深度强化学习（Deep-RL）为图上的组合优化（CO）问题推导启发式方法的趋势日益增长。最大覆盖问题（MCP）及其在社交网络上的概率变体——影响力最大化（IM），在这一研究方向中尤为突出。本文提出了一项全面的基准研究，深入探讨了五种近期提出的用于MCP和IM的Deep-RL方法的有效性与效率。这些方法发表于顶级数据科学会议，分别为S2V-DQN、Geometric-QN、GCOMB、RL4IM和LeNSE。我们的发现表明，在各种场景下，Lazy Greedy算法在MCP上始终优于所有Deep-RL方法。对于IM，像IMM和OPIM这样具有理论保证的算法在大多数场景中表现出优于Deep-RL方法的性能。值得注意的是，我们在IM问题中观察到一个异常现象：当影响力传播几乎不随预算增加而增加时，Deep-RL方法的表现会略优于IMM和OPIM。此外，实验结果突出了在实际环境中将Deep-RL方法应用于MCP和IM时的常见问题。最后，我们讨论了改进Deep-RL方法的潜在途径。我们的基准研究揭示了当前利用深度强化学习解决组合优化问题研究中的潜在挑战。
英文摘要：
Recent years have witnessed a growing trend toward employing deep reinforcement learning (Deep-RL) to derive heuristics for combinatorial optimization (CO) problems on graphs. Maximum Coverage Problem (MCP) and its probabilistic variant on social networks, Influence Maximization (IM), have been particularly prominent in this line of research. In this paper, we present a comprehensive benchmark study that thoroughly investigates the effectiveness and efficiency of five recent Deep-RL methods for MCP and IM. These methods were published in top data science venues, namely S2V-DQN, Geometric-QN, GCOMB, RL4IM, and LeNSE. Our findings reveal that, across various scenarios, the Lazy Greedy algorithm consistently outperforms all Deep-RL methods for MCP. In the case of IM, theoretically sound algorithms like IMM and OPIM demonstrate superior performance compared to Deep-RL methods in most scenarios. Notably, we observe an abnormal phenomenon in IM problem where Deep-RL methods slightly outperform IMM and OPIM when the influence spread nearly does not increase as the budget increases. Furthermore, our experimental results highlight common issues when applying Deep-RL methods to MCP and IM in practical settings. Finally, we discuss potential avenues for improving Deep-RL methods. Our benchmark study sheds light on potential challenges in current deep reinforcement learning research for solving combinatorial optimization problems.
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