19 citations · 77 across the 11 of their papers we have counts for
12 papers
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information
Zhendong Shi, Xiaoli Wei, Ercan E. Kuruoglu
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in ma…
Continuous-time q-learning for mean-field control problems
Xiaoli Wei, Xiang Yu
This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in th…
Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement Learning
Yuanquan Hu, Xiaoli Wei, Junji Yan +1
The marriage between mean-field theory and reinforcement learning has shown a great capacity to solve large-scale control problems with homogeneous agents. To break the homogeneity…
Thompson Sampling on Asymmetric -Stable Bandits
Zhendong Shi, Ercan E. Kuruoglu, Xiaoli Wei
In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important. Multi-armed bandit problem can optimize the pr…
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
Haotian Gu, Xin Guo, Xiaoli Wei +1
One of the challenges for multi-agent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial in…
Itô's formula for flows of measures on semimartingales
Xin Guo, Huyên Pham, Xiaoli Wei
We establish Itô's formula along flows of probability measures associated with general semimartingales; this generalizes existing results for flows of measures on Itô processes. Ou…
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Haotian Gu, Xin Guo, Xiaoli Wei +1
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality. This paper builds the mathematical framework to a…
Dynamic Programming Principles for Mean-Field Controls with Learning
Haotian Gu, Xin Guo, Xiaoli Wei +1
Dynamic programming principle (DPP) is fundamental for control and optimization, including Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean-fiel…
Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
Huyen Pham, Xiaoli Wei, Chao Zhou
This paper focuses on a dynamic multi-asset mean-variance portfolio selection problem under model uncertainty. We develop a continuous time framework for taking into account ambigu…
Dynamic programming for optimal control of stochastic McKean-Vlasov dynamics
Huyên Pham, Xiaoli Wei
We study the optimal control of general stochastic McKean-Vlasov equation. Such problem is motivated originally from the asymptotic formulation of cooperative equilibrium for a lar…
Bellman equation and viscosity solutions for mean-field stochastic control problem
Huyên Pham, Xiaoli Wei
We consider the stochastic optimal control problem of McKean-Vlasov stochastic differential equation where the coefficients may depend upon the joint law of the state and control.…
Discrete time McKean-Vlasov control problem: a dynamic programming approach
Huyên Pham, Xiaoli Wei
We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal d…