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07/25/2023
Settling the Sample Complexity of Online Reinforcement Learning
A central issue lying at the heart of online reinforcement learning (RL)...
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06/15/2023
Active Representation Learning for General Task Space with Applications in Robotics
Representation learning based on multi-task pretraining has become a pow...
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06/12/2023
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
We investigate learning the equilibria in non-stationary multi-agent sys...
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06/05/2023
Improved Active Multi-Task Representation Learning via Lasso
To leverage the copious amount of data from source tasks and overcome th...
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02/20/2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron
We revisit the problem of learning a single neuron with ReLU activation ...
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02/07/2023
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
We propose a new model, independent linear Markov game, for multi-agent ...
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02/03/2023
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
We study stochastic delayed feedback in general multi-agent sequential d...
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01/31/2023
Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
We study variance-dependent regret bounds for Markov decision processes ...
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01/27/2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
It is believed that Gradient Descent (GD) induces an implicit bias towar...
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10/24/2022
Offline congestion games: How feedback type affects data coverage requirement
This paper investigates when one can efficiently recover an approximate ...
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10/20/2022
Horizon-Free Reinforcement Learning for Latent Markov Decision Processes
We study regret minimization for reinforcement learning (RL) in Latent M...
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10/19/2022
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
Generalization in Reinforcement Learning (RL) aims to learn an agent dur...
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10/04/2022
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
We consider infinite-horizon discounted Markov decision processes and st...
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10/03/2022
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Multi-Agent Reinforcement Learning (MARL) – where multiple agents learn ...
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09/07/2022
Blessing of Class Diversity in Pre-training
This paper presents a new statistical analysis aiming to explain the rec...
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06/30/2022
Denoised MDPs: Learning World Models Better Than the World Itself
The ability to separate signal from noise, and reason with clean abstrac...
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06/17/2022
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization
We consider the smooth convex-concave bilinearly-coupled saddle-point pr...
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06/04/2022
Learning in Congestion Games with Bandit Feedback
Learning Nash equilibria is a central problem in multi-agent systems. In...
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06/01/2022
On Gap-dependent Bounds for Offline Reinforcement Learning
This paper presents a systematic study on gap-dependent sample complexit...
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06/01/2022
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
This paper considers offline multi-agent reinforcement learning. We prop...
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05/31/2022
Provable General Function Class Representation Learning in Multitask Bandits and MDPs
While multitask representation learning has become a popular approach in...
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05/26/2022
Variance-Aware Sparse Linear Bandits
It is well-known that the worst-case minimax regret for sparse linear ba...
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03/29/2022
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
We give novel algorithms for multi-task and lifelong linear bandits with...
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03/24/2022
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies
This paper gives the first polynomial-time algorithm for tabular Markov ...
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02/11/2022
Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems
Over the recent years, reinforcement learning (RL) has shown impressive ...
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02/04/2022
TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer
Car-following refers to a control process in which the following vehicle...
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02/02/2022
Active Multi-Task Representation Learning
To leverage the power of big data from source tasks and overcome the sca...
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01/26/2022
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Reward-free reinforcement learning (RL) considers the setting where the ...
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01/10/2022
When is Offline Two-Player Zero-Sum Markov Game Solvable?
We study what dataset assumption permits solving offline two-player zero...
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12/21/2021
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Policy optimization methods are one of the most widely used classes of R...
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12/07/2021
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Obtaining first-order regret bounds – regret bounds scaling not as the w...
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10/11/2021
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Non-contrastive methods of self-supervised learning (such as BYOL and Si...
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07/01/2021
Gap-Dependent Bounds for Two-Player Markov Games
As one of the most popular methods in the field of reinforcement learnin...
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06/27/2021
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
We study the asymmetric low-rank factorization problem: min_𝐔∈ℝ^m ×...
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06/22/2021
A Unified Framework for Conservative Exploration
We study bandits and reinforcement learning (RL) subject to a conservati...
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06/21/2021
Corruption Robust Active Learning
We conduct theoretical studies on streaming-based active learning for bi...
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06/15/2021
On the Power of Multitask Representation Learning in Linear MDP
While multitask representation learning has become a popular approach in...
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06/12/2021
Provable Adaptation across Multiway Domains via Representation Learning
This paper studies zero-shot domain adaptation where each domain is inde...
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04/22/2021
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
We study the problem of learning in the stochastic shortest path (SSP) s...
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03/25/2021
Nearly Horizon-Free Offline Reinforcement Learning
We revisit offline reinforcement learning on episodic time-homogeneous t...
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03/19/2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL
This work introduces Bilinear Classes, a new structural framework, which...
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02/19/2021
Randomized Exploration is Near-Optimal for Tabular MDP
We study exploration using randomized value functions in Thompson Sampli...
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02/17/2021
Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games
Policy gradient methods are widely used in solving two-player zero-sum g...
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02/13/2021
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
We study episodic reinforcement learning under unknown adversarial corru...
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02/09/2021
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap
This paper presents a new model-free algorithm for episodic finite-horiz...
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01/29/2021
Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP
We show how to construct variance-aware confidence sets for linear bandi...
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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...
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10/12/2020
Nearly Minimax Optimal Reward-free Reinforcement Learning
We study the reward-free reinforcement learning framework, which is part...
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09/28/2020
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Episodic reinforcement learning and contextual bandits are two widely st...
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09/24/2020