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07/26/2023
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks
We study reinforcement learning (RL) for learning a Quantal Stackelberg ...
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07/08/2023
Contextual Dynamic Pricing with Strategic Buyers
Personalized pricing, which involves tailoring prices based on individua...
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06/26/2023
A General Framework for Sequential Decision-Making under Adaptivity Constraints
We take the first step in studying general sequential decision-making un...
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06/21/2023
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable ...
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05/31/2023
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
We examine online safe multi-agent reinforcement learning using constrai...
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05/30/2023
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
In this paper, we conduct a comprehensive study of In-Context Learning (...
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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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05/29/2023
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Diffusion models have demonstrated highly-expressive generative capabili...
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05/29/2023
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism
In this paper, we study offline Reinforcement Learning with Human Feedba...
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05/08/2023
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
Policy optimization methods with function approximation are widely used ...
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03/28/2023
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Most offline reinforcement learning (RL) methods suffer from the trade-o...
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03/20/2023
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
We study the offline contextual bandit problem, where we aim to acquire ...
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03/15/2023
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
We study the incentivized information acquisition problem, where a princ...
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03/03/2023
Can We Find Nash Equilibria at a Linear Rate in Markov Games?
We study decentralized learning in two-player zero-sum discounted Markov...
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12/29/2022
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scal...
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12/23/2022
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
Motivated by the human-machine interaction such as training chatbots for...
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12/19/2022
Policy learning "without” overlap: Pessimism and generalized empirical Bernstein's inequality
This paper studies offline policy learning, which aims at utilizing obse...
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10/19/2022
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
We study reserve price optimization in multi-phase second price auctions...
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09/29/2022
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
It is quite challenging to ensure the safety of reinforcement learning (...
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09/20/2022
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
The cooperative Multi-A gent R einforcement Learning (MARL) with permuta...
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09/18/2022
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
We study the offline reinforcement learning (RL) in the face of unmeasur...
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08/23/2022
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
We study offline reinforcement learning under a novel model called strat...
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07/29/2022
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
In view of its power in extracting feature representation, contrastive s...
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07/25/2022
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
While single-agent policy optimization in a fixed environment has attrac...
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06/03/2022
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games
We study decentralized policy learning in Markov games where we control ...
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05/26/2022
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
We study offline reinforcement learning (RL) in partially observable Mar...
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05/26/2022
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Reinforcement learning in partially observed Markov decision processes (...
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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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05/05/2022
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
Dynamic mechanism design has garnered significant attention from both co...
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04/20/2022
Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations
Despite the success of reinforcement learning (RL) for Markov decision p...
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03/07/2022
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
We study a Markov matching market involving a planner and a set of strat...
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03/03/2022
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches
In this paper, we study the problem of regret minimization for episodic ...
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02/25/2022
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Dynamic mechanism design studies how mechanism designers should allocate...
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02/23/2022
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Offline Reinforcement Learning (RL) aims to learn policies from previous...
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02/22/2022
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
In today's economy, it becomes important for Internet platforms to consi...
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01/28/2022
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
In model-based reinforcement learning for safety-critical control system...
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12/28/2021
Exponential Family Model-Based Reinforcement Learning via Score Matching
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-...
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12/27/2021
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Actor-critic (AC) algorithms, empowered by neural networks, have had sig...
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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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11/06/2021
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
We study risk-sensitive reinforcement learning (RL) based on the entropi...
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10/24/2021
SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn the optimal policy fro...
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10/19/2021
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
To achieve sample efficiency in reinforcement learning (RL), it necessit...
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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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10/04/2021
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima
To regulate a social system comprised of self-interested agents, economi...
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08/19/2021
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
In generative adversarial imitation learning (GAIL), the agent aims to l...
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08/08/2021
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
The recent emergence of reinforcement learning has created a demand for ...
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07/30/2021
Towards General Function Approximation in Zero-Sum Markov Games
This paper considers two-player zero-sum finite-horizon Markov games wit...
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07/06/2021
A Unified Off-Policy Evaluation Approach for General Value Function
General Value Function (GVF) is a powerful tool to represent both the pr...
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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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06/15/2021
