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arXiv Machine Learning By Tomasz R. Bielecki, Thibaut Mastrolia, Haoze Yan

Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions

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The paper introduces a method for controlling multivariate Hawkes-driven stochastic differential equations using machine learning in a non‑Markovian setting. It develops a finite‑dimensional Markovianization technique that approximates Hawkes processes with mixtures of exponential kernels, proving convergence of the approximation to the original process and its value function. A continuous‑time deterministic policy gradient algorithm, called Hawkes‑CT DDPG, is then proposed to solve the control problem model‑free, relying only on observed event times, SDE solutions, and decay filters, and is compared against discrete‑time reinforcement learning approaches for various kernel types.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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