5 citations · 26 across the 23 of their papers we have counts for
23 papers
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
Haochen Zhang, Xi Chen, Lin F. Yang
Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi), allowing users to trade cryptocurrencies without the need for third-party authorization. Investors…
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Yunfan Li, Yiran Wang, Yu Cheng +1
Policy optimization methods are powerful algorithms in Reinforcement Learning (RL) for their flexibility to deal with policy parameterization and ability to handle model misspecifi…
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat +2
Large-scale language models have shown the ability to adapt to a new task via conditioning on a few demonstrations (i.e., in-context learning). However, in the vision-language doma…
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
Jiayi Huang, Han Zhong, Liwei Wang +1
While numerous works have focused on devising efficient algorithms for reinforcement learning (RL) with uniformly bounded rewards, it remains an open question whether sample or tim…
Replicability in Reinforcement Learning
Amin Karbasi, Grigoris Velegkas, Lin F. Yang +1
We initiate the mathematical study of replicability as an algorithmic property in the context of reinforcement learning (RL). We focus on the fundamental setting of discounted tabu…
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Dingwen Kong, Lin F. Yang
An appropriate reward function is of paramount importance in specifying a task in reinforcement learning (RL). Yet, it is known to be extremely challenging in practice to design a…
Does Sparsity Help in Learning Misspecified Linear Bandits?
Jialin Dong, Lin F. Yang
Recently, the study of linear misspecified bandits has generated intriguing implications of the hardness of learning in bandits and reinforcement learning (RL). In particular, Du e…
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
Osama A. Hanna, Lin F. Yang, Christina Fragouli
In this paper, we address the stochastic contextual linear bandit problem, where a decision maker is provided a context (a random set of actions drawn from a distribution). The exp…
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Osama A. Hanna, Lin F. Yang, Christina Fragouli
Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wirel…
Near-Optimal Sample Complexity Bounds for Constrained MDPs
Sharan Vaswani, Lin F. Yang, Csaba Szepesvári
In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (C…
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
Sanae Amani, Lin F. Yang, Ching-An Cheng
We study lifelong reinforcement learning (RL) in a regret minimization setting of linear contextual Markov decision process (MDP), where the agent needs to learn a multi-task polic…
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Sanae Amani, Tor Lattimore, András György +1
We study distributed contextual linear bandits with stochastic contexts, where agents act cooperatively to solve a linear bandit-optimization problem with -dimensional featu…
Solving Multi-Arm Bandit Using a Few Bits of Communication
Osama A. Hanna, Lin F. Yang, Christina Fragouli
The multi-armed bandit (MAB) problem is an active learning framework that aims to select the best among a set of actions by sequentially observing rewards. Recently, it has become…
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Recently there is a surge of interest in understanding the horizon-dependence of the sample complexity in reinforcement learning (RL). Notably, for an RL environment with horizon l…
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Han Zhong, Jiayi Huang, Lin F. Yang +1
Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise …
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation
Junhong Shen, Lin F. Yang
Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating deep neural networks into RL frameworks. However, these algorithms often require…
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao, Lin F. Yang, Kaiqing Zhang +1
Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential sample complexity dependence on the number of agents, a phenomenon known as \emph{the curse of…
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Xiaoyu Chen, Jiachen Hu, Lin F. Yang +1
Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to cou…
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
For the problem of task-agnostic reinforcement learning (RL), an agent first collects samples from an unknown environment without the supervision of reward signals, then is reveale…
Safe Reinforcement Learning with Linear Function Approximation
Sanae Amani, Christos Thrampoulidis, Lin F. Yang
Safety in reinforcement learning has become increasingly important in recent years. Yet, existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to…
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen +5
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlik…
Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal +1
Policy optimization methods remain a powerful workhorse in empirical Reinforcement Learning (RL), with a focus on neural policies that can easily reason over complex and continuous…
Towards Fundamental Limits of Multi-armed Bandits with Random Walk Feedback
Tianyu Wang, Lin F. Yang, Zizhuo Wang
In this paper, we consider a new Multi-Armed Bandit (MAB) problem where arms are nodes in an unknown and possibly changing graph, and the agent (i) initiates random walks over the…