30 citations · 109 across the 31 of their papers we have counts for
10 papers · 1 filter
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
Heyang Zhao, Jiafan He, Quanquan Gu
The exploration-exploitation dilemma has been a central challenge in reinforcement learning (RL) with complex model classes. In this paper, we propose a new algorithm, Monotonic Q-…
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
Qiwei Di, Heyang Zhao, Jiafan He +1
Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based on the data collected by a behavior policy, has attracted increasing attention in recent…
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
Yifei Min, Jiafan He, Tianhao Wang +1
We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We pro…
Integrating Tick-level Data and Periodical Signal for High-frequency Market Making
Jiafa He, Cong Zheng, Can Yang
We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and sellin…
Optimal Execution Using Reinforcement Learning
Cong Zheng, Jiafa He, Can Yang
This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrenc…
Option Dynamic Hedging Using Reinforcement Learning
Cong Zheng, Jiafa He, Can Yang
This work focuses on the dynamic hedging of financial derivatives, where a reinforcement learning algorithm is designed to minimize the variance of the delta hedging process. In co…
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Kaixuan Ji, Qingyue Zhao, Jiafan He +2
Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by , and proved regret bounds that have a polyloga…
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Yue Wu, Jiafan He, Quanquan Gu
Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation. However, all these works only provide regret or sample complexity g…
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
Weitong Zhang, Jiafan He, Zhiyuan Fan +1
We study linear contextual bandits in the misspecified setting, where the expected reward function can be approximated by a linear function class up to a bounded misspecification l…
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
Heyang Zhao, Jiafan He, Dongruo Zhou +2
Recently, several studies (Zhou et al., 2021a; Zhang et al., 2021b; Kim et al., 2021; Zhou and Gu, 2022) have provided variance-dependent regret bounds for linear contextual bandit…