1 citations · 1 across the 7 of their papers we have counts for
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Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach
Chenbei Lu, Zaiwei Chen, Tongxin Li +2
Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models. In many real-world applications…
Safe Exploitative Play with Untrusted Type Beliefs
Tongxin Li, Tinashe Handina, Shaolei Ren +1
The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors giv…
Building Socially-Equitable Public Models
Yejia Liu, Jianyi Yang, Pengfei Li +2
Public models offer predictions to a variety of downstream tasks and have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. Ho…
Anytime-Competitive Reinforcement Learning with Policy Prior
Jianyi Yang, Pengfei Li, Tongxin Li +2
This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP). Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the expected…