17 citations · 17 across the 7 of their papers we have counts for
7 papers
Merton's Problem with Recursive Perturbed Utility
Min Dai, Yuchao Dong, Yanwei Jia +1
The classical Merton investment problem predicts deterministic, state-dependent portfolio rules; however, laboratory and field evidence suggests that individuals often prefer rando…
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
Yilie Huang, Yanwei Jia, Xun Yu Zhou
We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes, ye…
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
Yilie Huang, Yanwei Jia, Xun Yu Zhou
We study reinforcement learning (RL) for a class of continuous-time linear-quadratic (LQ) control problems for diffusions, where states are scalar-valued and running control reward…
Data-Driven Merton's Strategies via Policy Randomization
Min Dai, Yuchao Dong, Yanwei Jia +1
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primi…
q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang e…
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
Yanwei Jia, Xun Yu Zhou
We study policy gradient (PG) for reinforcement learning in continuous time and space under the regularized exploratory formulation developed by Wang et al. (2020). We represent th…
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
Yanwei Jia, Xun Yu Zhou
We propose a unified framework to study policy evaluation (PE) and the associated temporal difference (TD) methods for reinforcement learning in continuous time and space. We show…