English(EN) When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game

强化学习在复杂的金融交易动态中面临挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员探索了近端策略优化（PPO），一种强化学习算法，在具有分析求解动态的连续时间经纪人-交易者博弈中的应用。他们发现，虽然PPO在简单条件下可以近似最优策略，但在涉及随机订单流的复杂场景中却举步维艰。研究还表明，分析解可以作为诊断RL性能的宝贵基准，并作为策略适应的有效起点。 AI

影响评估了当前强化学习算法在复杂、可分析求解的金融市场中的局限性。

排序理由学术论文，详细介绍了强化学习在新颖应用和特定领域的评估。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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Newsworthiness bucket: Tool; 学术论文，详细介绍了强化学习在新颖应用和特定领域的评估。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Siu Tung Wong (Institute of Finance and Technology, University College London), Carlo Campajola (Institute of Finance and Technology, University College London, UZH Blockchain Center) · 2026-10-05 04:00

当正确的奖励不足以：分析性解决的经纪人-交易者博弈中PPO的诊断与引导

arXiv:2610.03598v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to obtain. There are financial mathematics literactures which provides many solved …