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08/18/2023
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Large language models (LLMs), such as GPT-4, have shown remarkable perfo...
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05/27/2023
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Existing online learning algorithms for adversarial Markov Decision Proc...
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05/24/2023
Regret Matching+: (In)Stability and Fast Convergence in Games
Regret Matching+ (RM+) and its variants are important algorithms for sol...
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03/05/2023
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games
We revisit the problem of learning in two-player zero-sum Markov games, ...
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02/27/2023
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
We study the problem of designing adaptive multi-armed bandit algorithms...
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02/02/2023
Average-Constrained Policy Optimization
Reinforcement Learning (RL) with constraints is becoming an increasingly...
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10/23/2022
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Supply chain management (SCM) has been recognized as an important discip...
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10/04/2022
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
We study high-probability regret bounds for adversarial K-armed bandits ...
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08/31/2022
Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games
A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled ...
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06/17/2022
Near-Optimal No-Regret Learning for General Convex Games
A recent line of work has established uncoupled learning dynamics such t...
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05/26/2022
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
We consider regret minimization for Adversarial Markov Decision Processe...
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05/25/2022
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments
We initiate the study of dynamic regret minimization for goal-oriented r...
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04/25/2022
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games
In this paper we establish efficient and uncoupled learning dynamics so ...
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02/12/2022
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
We consider the problem of combining and learning over a set of adversar...
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02/12/2022
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
We consider the problem of adversarial bandit convex optimization, that ...
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02/07/2022
Policy Optimization for Stochastic Shortest Path
Policy optimization is among the most popular and successful reinforceme...
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02/01/2022
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
While extensive-form games (EFGs) can be converted into normal-form game...
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01/31/2022
Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints
We study regret minimization for infinite-horizon average-reward Markov ...
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01/31/2022
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
The standard assumption in reinforcement learning (RL) is that agents ob...
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01/30/2022
No-Regret Learning in Time-Varying Zero-Sum Games
Learning from repeated play in a fixed two-player zero-sum game is a cla...
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12/18/2021
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
We introduce two new no-regret algorithms for the stochastic shortest pa...
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07/18/2021
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
Policy optimization is a widely-used method in reinforcement learning. D...
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06/27/2021
Last-iterate Convergence in Extensive-Form Games
Regret-based algorithms are highly efficient at finding approximate Nash...
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06/15/2021
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path
We introduce a generic template for developing regret minimization algor...
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06/09/2021
Online Learning for Stochastic Shortest Path Model via Posterior Sampling
We consider the problem of online reinforcement learning for the Stochas...
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06/08/2021
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
We consider the best-of-both-worlds problem for learning an episodic Mar...
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02/11/2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
In this work, we develop linear bandit algorithms that automatically ada...
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02/10/2021
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach
We propose a black-box reduction that turns a certain reinforcement lear...
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02/10/2021
Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case
We make significant progress toward the stochastic shortest path problem...
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02/08/2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
We study infinite-horizon discounted two-player zero-sum Markov games, a...
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02/01/2021
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
We resolve the long-standing "impossible tuning" issue for the classic e...
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12/07/2020
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
We study the stochastic shortest path problem with adversarial costs and...
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07/23/2020
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
We develop several new algorithms for learning Markov Decision Processes...
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07/16/2020
Comparator-adaptive Convex Bandits
We study bandit convex optimization methods that adapt to the norm of th...
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06/25/2020
Active Online Domain Adaptation
Online machine learning systems need to adapt to domain shifts. Meanwhil...
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06/19/2020
Open Problem: Model Selection for Contextual Bandits
In statistical learning, algorithms for model selection allow the learne...
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06/16/2020
Linear Last-iterate Convergence for Matrix Games and Stochastic Games
Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...
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06/14/2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
We develop a new approach to obtaining high probability regret bounds fo...
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06/10/2020
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
This work studies the problem of learning episodic Markov Decision Proce...
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06/08/2020
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret
Recently, model-free reinforcement learning has attracted research atten...
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03/07/2020
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
We revisit the problem of online learning with sleeping experts/bandits:...
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03/04/2020
Taking a hint: How to leverage loss predictors in contextual bandits?
We initiate the study of learning in contextual bandits with the help of...
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02/02/2020
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
We study small-loss bounds for the adversarial multi-armed bandits probl...
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12/13/2019
Fair Contextual Multi-Armed Bandits: Theory and Experiments
When an AI system interacts with multiple users, it frequently needs to ...
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12/03/2019
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
We consider the problem of learning in episodic finite-horizon Markov de...
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10/15/2019
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Model-free reinforcement learning is known to be memory and computation ...
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06/03/2019
Model selection for contextual bandits
We introduce the problem of model selection for contextual bandits, wher...
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05/30/2019
Equipping Experts/Bandits with Long-term Memory
We propose the first reduction-based approach to obtaining long-term mem...
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04/09/2019
Hypothesis Set Stability and Generalization
We present an extensive study of generalization for data-dependent hypot...
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02/03/2019