ABIDES-MARL：一种用于内生流动性下最优执行的多智能体强化学习环境
ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Optimal Execution with Endogenous Liquidity
AI总结：
ABIDES-MARL是用于内生流动性下最优执行的多智能体强化学习框架，扩展ABIDES-Gym实现多学习智能体，在扩展Kyle场景验证其可复现性，所得策略与外生冲击基准策略差异显著，为金融领域可解释智能体AI提供基础。
摘要：
经典最优执行模型将市场冲击视为预先设定的外生过程，但当做市商采取策略性调整时，该假设会成为结构性误设：执行动态取决于其他智能体的策略与行动，因此该问题不再是单智能体控制问题，而变为有限时间随机博弈，其中流动性内生地产生于异质性市场参与者间的互动。我们因此推出ABIDES-MARL，这是一种用于在真实限价订单簿环境中研究内生流动性下最优执行的多智能体强化学习框架。该框架扩展了ABIDES-Gym，以支持多个学习智能体，其同步决策周期能保留恰当的信息过滤和关键市场微观结构特征。我们在扩展的Kyle模型场景中验证了该框架，多个学习智能体实现了渐进式价格发现。数值结果显示，在内生、适应性流动性下学习到的执行策略，与固定外生价格冲击基准所隐含的策略存在根本差异。特别地，在外生冲击下优化的策略，一旦做市商采取策略性调整就会表现不佳：当交易员与做市商间的信息不平衡时，做市商会利用可预测的执行行为，市场动态可能会退化。该框架因此为研究真实市场中的策略性调整提供了可复现的基础，并推动金融领域具有经济可解释性的智能体AI系统的发展。
英文摘要：
Classical optimal execution models treat market impact as a pre-specified, exogenous process. However, when market makers adapt strategically, this assumption becomes a structural misspecification: execution dynamics depend on the policies and actions of other agents. The problem therefore ceases to be a single-agent control problem and instead becomes a finite-horizon stochastic game, in which liquidity emerges endogenously from the interactions among heterogeneous market players. We hence introduce ABIDES-MARL, a multi-agent reinforcement learning framework for studying optimal execution under endogenous liquidity in a realistic limit order book setting. The framework extends ABIDES-Gym to support multiple learning agents with synchronized decision periods that preserve proper information filtration and key market microstructure features. We validate the framework in an extended Kyle setting, where multiple learning agents recovers gradual price discovery. Numerical results then show that execution strategies learned under endogenous, adaptive liquidity differ fundamentally from those implied by fixed exogenous price-impact benchmarks. In particular, strategies optimized under exogenous impact perform poorly once market makers adapt strategically: when information is not balanced across traders and market makers, market makers exploit predictable execution and the market dynamics may degenerate. The framework therefore provides a reproducible foundation for studying strategic adaptation in realistic markets and contributes to the development of economically interpretable agentic AI systems in finance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
