Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions
Score
73首次将连续时间强化学习系统应用于非马尔可夫Hawkes跳跃扩散控制，提出新算法和收敛性理论，信息增量大。
理论证明完整，实验对比多种核函数，结构严谨，方法细节清晰。
无模型算法可应用于金融、网络等事件驱动场景，实用性较强。
arXiv预印本，日期疑似为未来编号，但内容紧跟强化学习与随机控制前沿。
来源为arXiv AI/CL/LG，属于权威预印本平台，且作者有完整学术论证。
Base 81.5 × T2 Source weight ×0.9 = 73
Curation line 70Selected
Why selected
Strong methodological innovation with theoretical proofs and empirical validation, significantly advancing the intersection of continuous-time stochastic control and reinforcement learning.
AI commentary
「The most noteworthy aspect is moving reinforcement learning beyond discrete-time paradigms to handle Hawkes processes with memory. If you work on high-frequency trading or event sequence modeling, this model-free continuous-time control method may be more suitable for real-world systems than off-the-shelf DQN-style tools.」
Quick overview
This study addresses stochastic control of multivariate Hawkes-driven stochastic differential equations using machine learning algorithms in a non-Markovian setting. Due to the path dependence of Hawkes intensity memory, the problem does not fit classical stochastic control theory. The authors develop a finite-dimensional Markovianization procedure approximating multivariate Hawkes processes with mixtures of exponential kernels and prove convergence. They then formulate continuous-time deterministic policy gradient learning, called Hawkes-CT DDPG, which solves the non-Markovian optimization model-free by observing only event times, SDE realizations, and decay filters, without knowing Hawkes kernel coefficients. Comparisons against discrete-time reinforcement learning under exponential, Erlang, and power-law kernels demonstrate the effectiveness of the proposed method.
- Proposes a finite-dimensional Markovianization method for Hawkes jump-diffusions, proving convergence of the process, intensity, and value function
- Designs Hawkes-CT DDPG, a continuous-time deterministic policy gradient algorithm for model-free learning
- Requires only event times, SDE solutions, and decay filters, without knowing Hawkes kernel coefficients
- Demonstrates advantages over discrete-time RL under exponential, Erlang, and power-law kernels
Full text
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