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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...
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05/29/2023
One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration
In online reinforcement learning (online RL), balancing exploration and ...
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02/21/2023
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
While quantum reinforcement learning (RL) has attracted a surge of atten...
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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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10/27/2022
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Sim-to-real transfer trains RL agents in the simulated environments and ...
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05/27/2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
It is well-known that modern neural networks are vulnerable to adversari...
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05/23/2022
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
We study human-in-the-loop reinforcement learning (RL) with trajectory p...
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12/27/2021
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?
We study multi-player general-sum Markov games with one of the players d...
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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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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...
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10/18/2021
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
We study episodic reinforcement learning (RL) in non-stationary linear k...
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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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12/28/2020