AI 中文总结
研究在闭环去中心化交易所模拟器中，针对动态费用下的执行问题，采用构建最小闭环模拟器及小深度Q网络的方法，得出该方法能减少执行缺口，且优势集中在动态费用环境中的结论。
AI 中文摘要
面向交易者的动态费用越来越多地被用于自动化做市商（AMM），但历史数据无法确定订单流将如何响应，因为面向交易者的费用不变、交易者类型是潜在的，且重放的磁带不是顺序决策环境。因此构建了一个最小闭环模拟器，其中存在缺失信号：两个由均衡启发的动态费用规则重新定价的常数乘积池、费用敏感噪声流和封闭形式的CEX-AMM套利。均衡用作封闭原则而非交易者学习的对象。与调优的基准调度、规划、前瞻和表格策略相比，一个小的深度Q网络是唯一评估有效的策略，在每个测试的步内排序下，它能减少执行缺口，在特定的代理最后排序下，订单名义金额可减少13.3个基点，且优势集中在动态费用环境中并从中学习。结果是关于AMM中执行控制的模型条件反事实证据，而非关于历史交易者、均衡玩法或可部署利润的证据。
英文摘要
Trader-facing dynamic fees are increasingly proposed for automated market makers (AMMs), but historical data do not identify how order flow would respond: trader-facing fees do not vary, trader types are latent, and a replayed tape is not a sequential decision environment. We therefore construct a minimal closed-loop simulator in which the missing signal exists by construction: two constant-product pools repriced by an equilibrium-inspired dynamic-fee rule, fee-sensitive noise flow, and closed-form CEX--AMM arbitrage. Equilibrium is used as a closure principle, not as an object the trader learns. Against a tuned benchmark ladder of schedule, planning, lookahead, and tabular policies, a small DQN is the only evaluated valid policy whose paired improvement over tuned one-step routing excludes zero. On a reserved final block of 1{,}000 seeds with completion forced to 1.0 for every policy, it reduces implementation shortfall under every tested intra-step ordering, by $13.3\bps$ of order notional under the pre-specified agent-last ordering, and the edge is concentrated in, and learned from, dynamic-fee environments: under constant fees the paired difference is indistinguishable from zero. The result is model-conditioned counterfactual evidence about execution control in AMMs, not evidence about historical traders, equilibrium play, or deployable profit.
