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06/25/2023
Is RLHF More Difficult than Standard RL?
Reinforcement learning from Human Feedback (RLHF) learns from preference...
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06/22/2023
Context-lumpable stochastic bandits
We consider a contextual bandit problem with S contexts and A actions. I...
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05/25/2023
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
This paper proposes a new easy-to-implement parameter-free gradient-base...
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05/18/2023
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
While policy optimization algorithms have played an important role in re...
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04/10/2023
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Generating human-like behavior on robots is a great challenge especially...
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03/02/2023
On the Provable Advantage of Unsupervised Pretraining
Unsupervised pretraining, which learns a useful representation using a l...
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02/13/2023
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the c...
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02/09/2023
Efficient displacement convex optimization with particle gradient descent
Particle gradient descent, which uses particles to represent a probabili...
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10/30/2022
Representation Learning for General-sum Low-rank Markov Games
We study multi-agent general-sum Markov games with nonlinear function ap...
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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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10/20/2022
Learning Rationalizable Equilibria in Multiplayer Games
A natural goal in multiagent learning besides finding equilibria is to l...
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09/29/2022
Optimistic MLE – A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
This paper introduces a simple efficient learning algorithms for general...
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09/06/2022
Faster federated optimization under second-order similarity
Federated learning (FL) is a subfield of machine learning where multiple...
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07/18/2022
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games
This paper proposes novel, end-to-end deep reinforcement learning algori...
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06/02/2022
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
This paper considers the challenging tasks of Multi-Agent Reinforcement ...
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05/30/2022
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent
A conceptually appealing approach for learning Extensive-Form Games (EFG...
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04/19/2022
When Is Partially Observable Reinforcement Learning Not Scary?
Applications of Reinforcement Learning (RL), in which agents learn to ma...
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03/14/2022
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
An ideal strategy in zero-sum games should not only grant the player an ...
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02/08/2022
Provable Reinforcement Learning with a Short-Term Memory
Real-world sequential decision making problems commonly involve partial ...
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02/03/2022
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
This paper resolves the open question of designing near-optimal algorith...
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12/23/2021
Globally convergent visual-feature range estimation with biased inertial measurements
The design of a globally convergent position observer for feature points...
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10/27/2021
V-Learning – A Simple, Efficient, Decentralized Algorithm for Multiagent RL
A major challenge of multiagent reinforcement learning (MARL) is the cur...
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10/07/2021
Understanding Domain Randomization for Sim-to-real Transfer
Reinforcement learning encounters many challenges when applied directly ...
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07/12/2021
A Simple Reward-free Approach to Constrained Reinforcement Learning
In constrained reinforcement learning (RL), a learning agent seeks to no...
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06/07/2021
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Modern reinforcement learning (RL) commonly engages practical problems w...
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06/02/2021
Minimax Optimization with Smooth Algorithmic Adversaries
This paper considers minimax optimization min_x max_y f(x, y) in the cha...
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03/25/2021
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
This paper considers batch Reinforcement Learning (RL) with general valu...
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02/23/2021
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involv...
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02/08/2021
Near-optimal Representation Learning for Linear Bandits and Linear RL
This paper studies representation learning for multi-task linear bandits...
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02/04/2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
While over-parameterization is widely believed to be crucial for the suc...
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02/01/2021
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
Finding the minimal structural assumptions that empower sample-efficient...
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11/09/2020
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
The classical theory of reinforcement learning (RL) has focused on tabul...
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10/04/2020
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Model-based algorithms—algorithms that decouple learning of the model an...
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06/22/2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Partial observability is a common challenge in many reinforcement learni...
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06/22/2020
Near-Optimal Reinforcement Learning with Self-Play
This paper considers the problem of designing optimal algorithms for rei...
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06/20/2020
On the Theory of Transfer Learning: The Importance of Task Diversity
We provide new statistical guarantees for transfer learning via represen...
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02/26/2020
Provable Meta-Learning of Linear Representations
Meta-learning, or learning-to-learn, seeks to design algorithms that can...
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02/10/2020
Provable Self-Play Algorithms for Competitive Reinforcement Learning
Self-play, where the algorithm learns by playing against itself without ...
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02/07/2020
Reward-Free Exploration for Reinforcement Learning
Exploration is widely regarded as one of the most challenging aspects of...
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02/05/2020
Near-Optimal Algorithms for Minimax Optimization
This paper resolves a longstanding open question pertaining to the desig...
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12/12/2019
Provably Efficient Exploration in Policy Optimization
While policy-based reinforcement learning (RL) achieves tremendous succe...
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07/11/2019
Provably Efficient Reinforcement Learning with Linear Function Approximation
Modern Reinforcement Learning (RL) is commonly applied to practical prob...
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06/02/2019
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
We consider nonconvex-concave minimax problems, _x_y∈Y f(x, y), where f ...
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02/13/2019
Stochastic Gradient Descent Escapes Saddle Points Efficiently
This paper considers the perturbed stochastic gradient descent algorithm...
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02/11/2019
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
In this note, we derive concentration inequalities for random vectors wi...
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02/02/2019
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal
Minmax optimization, especially in its general nonconvex-nonconcave form...
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11/20/2018
Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provide...
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07/10/2018
Is Q-learning Provably Efficient?
Model-free reinforcement learning (RL) algorithms, such as Q-learning, d...
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04/04/2018
Stability and Convergence Trade-off of Iterative Optimization Algorithms
The overall performance or expected excess risk of an iterative machine ...
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03/25/2018
