用闭环模拟器研究动态费率下交易执行，发现强化学习可显著降低滑点。
Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator
- 构建动态费率下的闭环交易模拟环境，包含套利机制与噪声订单流。
- DQN策略相比基准减少13.3个基点的执行损失，且仅在动态费率下有效。
- 适合关注AMM执行优化与强化学习应用的研究者。
面向交易者的动态费率正被提议用于自动化做市商（AMMs），但历史数据无法揭示订单流如何响应：交易费用不变化、交易者类型不可观测，回放交易记录也不构成序列决策环境。为此，我们构建了一个最小闭环模拟器，通过构造使缺失信号显式存在：两个由均衡启发的动态费率规则重定价的恒定乘积池、对费率敏感的噪声订单流，以及闭式解的中心化交易所-去中心化交易所套利模型。均衡作为闭合原则，而非交易者需学习的目标。在对比经过调优的阶梯式、规划型、前瞻型及表格型策略后，唯一表现有效的策略是小型DQN，其相对于单步路由策略的改进在所有测试条件下均显著优于零。在预留的最后1000个种子中，强制所有策略完成度为1.0，其在每种步骤内排序下均降低实施滑点，尤其在预设的代理最后排序下，实现13.3个基点的订单面值减损。该优势集中于并源自动态费率环境；在固定费率下，配对差异不可区分地接近零。结果提供关于AMM执行控制的模型条件反事实证据，而非关于历史交易者、均衡博弈或可部署利润的证据。
原文摘要 · Abstract (English)
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.
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