42 citations · 97 across the 17 of their papers we have counts for
5 papers · 1 filter
-Policy Gradient for Online Pricing
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an -policy gradient algorithm for the online pricing learning task. The…
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms. Herein agent interacts…
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting. We then apply it to study finite-time horizon stochastic control prob…
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
Conditional Sig-Wasserstein GANs for Time Series Generation
Shujian Liao, Hao Ni, Lukasz Szpruch +3
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle t…