18 citations · 33 across the 7 of their papers we have counts for
12 papers · 1 filter
Is Elo Rating Reliable? A Study Under Model Misspecification
Shange Tang, Yuanhao Wang, Chi Jin
Elo rating, widely used for skill assessment across diverse domains ranging from competitive games to large language models, is often understood as an incremental update algorithm…
Is RLHF More Difficult than Standard RL?
Yuanhao Wang, Qinghua Liu, Chi Jin
Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward signals. Preferences arguab…
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
Yuanhao Wang, Qinghua Liu, Yu Bai +1
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing lea…
Learning Rationalizable Equilibria in Multiplayer Games
Yuanhao Wang, Dingwen Kong, Yu Bai +1
A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in t…
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
Qinghua Liu, Yuanhao Wang, Chi Jin
An ideal strategy in zero-sum games should not only grant the player an average reward no less than the value of Nash equilibrium, but also exploit the (adaptive) opponents when th…
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
Chi Jin, Qinghua Liu, Yuanhao Wang +1
A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents, where the size of the joint action space scales exponentially with the number of agents.…