1 citations · 1 across the 18 of their papers we have counts for
7 papers · 2 filters
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
Avinandan Bose, Zhihan Xiong, Aadirupa Saha +2
Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on exten…
Learning to Cooperate with Humans using Generative Agents
Yancheng Liang, Daphne Chen, Abhishek Gupta +2
Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partne…
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
Natalia Zhang, Xinqi Wang, Qiwen Cui +3
We initiate the study of Preference-Based Multi-Agent Reinforcement Learning (PbMARL), exploring both theoretical foundations and empirical validations. We define the task as ident…
Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixture Models
Weihang Xu, Maryam Fazel, Simon S. Du
We study the gradient Expectation-Maximization (EM) algorithm for Gaussian Mixture Models (GMM) in the over-parameterized setting, where a general GMM with components learns…
Distributional Successor Features Enable Zero-Shot Policy Optimization
Chuning Zhu, Xinqi Wang, Tyler Han +2
Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in princi…
Offline Multi-task Transfer RL with Representational Penalization
Avinandan Bose, Simon Shaolei Du, Maryam Fazel
We study the problem of representation transfer in offline Reinforcement Learning (RL), where a learner has access to episodic data from a number of source tasks collected a priori…
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Yan Dai, Qiwen Cui, Simon S. Du
Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL). It was long believed that the "curse of multi-agents" (i.e., the algorithmic performance drop…