research
∙
09/18/2023
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
Decentralized exchanges (DEXs) are a cornerstone of decentralized financ...
share
research
∙
07/11/2023
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
Recently, DARPA launched the ShELL program, which aims to explore how ex...
research
∙
06/12/2023
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
While numerous works have focused on devising efficient algorithms for r...
research
∙
06/02/2023
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
Large-scale language models have shown the ability to adapt to a new tas...
research
∙
05/31/2023
Replicability in Reinforcement Learning
We initiate the mathematical study of replicability as an algorithmic pr...
research
∙
04/18/2023
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
An appropriate reward function is of paramount importance in specifying ...
research
∙
03/29/2023
Does Sparsity Help in Learning Misspecified Linear Bandits?
Recently, the study of linear misspecified bandits has generated intrigu...
research
∙
12/01/2022
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP
This work considers the sample complexity of obtaining an ε-optimal poli...
research
∙
11/08/2022
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
In this paper, we address the stochastic contextual linear bandit proble...
research
∙
06/13/2022
Near-Optimal Sample Complexity Bounds for Constrained MDPs
In contrast to the advances in characterizing the sample complexity for ...
research
∙
06/08/2022
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Contextual linear bandits is a rich and theoretically important model th...
research
∙
06/01/2022
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
We study lifelong reinforcement learning (RL) in a regret minimization s...
research
∙
05/26/2022
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
We study distributed contextual linear bandits with stochastic contexts,...
research
∙
11/11/2021
Solving Multi-Arm Bandit Using a Few Bits of Communication
The multi-armed bandit (MAB) problem is an active learning framework tha...
research
∙
11/01/2021
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning
Recently there is a surge of interest in understanding the horizon-depen...
research
∙
10/26/2021
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Despite a large amount of effort in dealing with heavy-tailed error in m...
research
∙
10/12/2021
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) algorithms often suffer from a...
research
∙
10/09/2021
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation
Recently, deep reinforcement learning (RL) has achieved remarkable empir...
research
∙
10/07/2021
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Although model-based reinforcement learning (RL) approaches are consider...
research
∙
08/11/2021
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
For the problem of task-agnostic reinforcement learning (RL), an agent f...
research
∙
06/15/2021
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the...
research
∙
06/14/2021
Online Sub-Sampling for Reinforcement Learning with General Function Approximation
Designing provably efficient algorithms with general function approximat...
research
∙
06/11/2021
Safe Reinforcement Learning with Linear Function Approximation
Safety in reinforcement learning has become increasingly important in re...
research
∙
06/11/2021
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
Graph neural networks (GNNs) are powerful tools for learning from graph ...
research
∙
03/22/2021
Provably Correct Optimization and Exploration with Non-linear Policies
Policy optimization methods remain a powerful workhorse in empirical Rei...
research
∙
02/25/2021
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally
We study the statistical limits of Imitation Learning (IL) in episodic M...
research
∙
01/02/2021
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost
Many real-world applications, such as those in medical domains, recommen...
research
∙
11/29/2020
Minimax Sample Complexity for Turn-based Stochastic Game
The empirical success of Multi-agent reinforcement learning is encouragi...
research
∙
11/25/2020
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where t...
research
∙
11/03/2020
Episodic Linear Quadratic Regulators with Low-rank Transitions
Linear Quadratic Regulators (LQR) achieve enormous successful real-world...
research
∙
11/03/2020
Random Walk Bandits
Bandit learning problems find important applications ranging from medica...
research
∙
10/12/2020
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
It is believed that a model-based approach for reinforcement learning (R...
research
∙
09/13/2020
Toward the Fundamental Limits of Imitation Learning
Imitation learning (IL) aims to mimic the behavior of an expert policy i...
research
∙
08/15/2020
Obtaining Adjustable Regularization for Free via Iterate Averaging
Regularization for optimization is a crucial technique to avoid overfitt...
research
∙
07/15/2020
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Model-based reinforcement learning (RL), which finds an optimal policy u...
research
∙
06/19/2020
On Reward-Free Reinforcement Learning with Linear Function Approximation
Reward-free reinforcement learning (RL) is a framework which is suitable...
research
∙
06/16/2020
Q-learning with Logarithmic Regret
This paper presents the first non-asymptotic result showing that a model...
research
∙
06/16/2020
Preference-based Reinforcement Learning with Finite-Time Guarantees
Preference-based Reinforcement Learning (PbRL) replaces reward values in...
research
∙
06/01/2020
Model-Based Reinforcement Learning with Value-Targeted Regression
This paper studies model-based reinforcement learning (RL) for regret mi...
research
∙
05/21/2020
Provably Efficient Reinforcement Learning with General Value Function Approximation
Value function approximation has demonstrated phenomenal empirical succe...
research
∙
05/01/2020
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
Learning to plan for long horizons is a central challenge in episodic re...
research
∙
03/15/2020
Provably Efficient Exploration for RL with Unsupervised Learning
We study how to use unsupervised learning for efficient exploration in r...
research
∙
02/23/2020
Sketching Transformed Matrices with Applications to Natural Language Processing
Suppose we are given a large matrix A=(a_i,j) that cannot be stored in m...
research
∙
12/06/2019
Does Knowledge Transfer Always Help to Learn a Better Policy?
One of the key approaches to save samples when learning a policy for a r...
research
∙
10/30/2019
Continuous Control with Contexts, Provably
A fundamental challenge in artificial intelligence is to build an agent ...
research
∙
10/07/2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Modern deep learning methods provide an effective means to learn good re...
research
∙
10/04/2019
Efficient Symmetric Norm Regression via Linear Sketching
We provide efficient algorithms for overconstrained linear regression pr...
research
∙
08/29/2019
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity
In this paper, we settle the sampling complexity of solving discounted t...
research
∙
06/10/2019
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
This work considers the sample complexity of obtaining an ϵ-optimal poli...
research
∙
06/02/2019