1 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
Laixi Shi, Jingchu Gai, Eric Mazumdar +2
Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. To address this, distributionally robust Markov games (RMGs) have been proposed to…
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
Laixi Shi, Eric Mazumdar, Yuejie Chi +1
To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studi…
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
Shicong Cen, Jincheng Mei, Katayoon Goshvadi +6
Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of pr…
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
He Wang, Laixi Shi, Yuejie Chi
In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between t…
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi +1
Offline reinforcement learning (RL), which seeks to learn an optimal policy using offline data, has garnered significant interest due to its potential in critical applications wher…
Beyond Expectations: Learning with Stochastic Dominance Made Practical
Shicong Cen, Jincheng Mei, Hanjun Dai +3
Stochastic dominance serves as a general framework for modeling a broad spectrum of decision preferences under uncertainty, with risk aversion as one notable example, as it natural…