1 citations · 3 across the 13 of their papers we have counts for
13 papers
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
Shangding Gu, Laixi Shi, Muning Wen +5
Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment s…
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
Tong Yang, Bo Dai, Lin Xiao +1
Multi-agent reinforcement learning (MARL) lies at the heart of a plethora of applications involving the interaction of a group of agents in a shared unknown environment. A prominen…
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…
Communication-Efficient Federated Optimization over Semi-Decentralized Networks
He Wang, Yuejie Chi
In large-scale federated and decentralized learning, communication efficiency is one of the most challenging bottlenecks. While gossip communication -- where agents can exchange in…
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Tong Yang, Shicong Cen, Yuting Wei +2
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a mult…
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression
Sijin Chen, Zhize Li, Yuejie Chi
We consider the problem of finding second-order stationary points of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees,…
Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control
Shicong Cen, Yuejie Chi
Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential d…
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
Pedro Valdeira, Yuejie Chi, Cláudia Soares +1
Most federated learning (FL) methods use a client-server scheme, where clients communicate only with a central server. However, this scheme is prone to bandwidth bottlenecks at the…