1 citations · 3 across the 9 of their papers we have counts for
9 papers
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration
Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen +3
Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MAR…
Learning of Nash Equilibria in Risk-Averse Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically,…
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
Yi Shen, Pan Xu, Michael M. Zavlanos
Off-policy evaluation and learning are concerned with assessing a given policy and learning an optimal policy from offline data without direct interaction with the environment. Oft…
Convergence Analysis of the Best Response Algorithm for Time-Varying Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe ot…
Asymmetric Feedback Learning in Online Convex Games
Zifan Wang, Xinlei Yi, Yi Shen +2
This paper considers convex games involving multiple agents that aim to minimize their own cost functions using locally available information. A common assumption in the study of s…
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health
Yi Shen, Jessilyn Dunn, Michael M. Zavlanos
In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizi…
A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games
Zifan Wang, Yi Shen, Zachary I. Bell +3
We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the…
Risk-Averse No-Regret Learning in Online Convex Games
Zifan Wang, Yi Shen, Michael M. Zavlanos
We consider an online stochastic game with risk-averse agents whose goal is to learn optimal decisions that minimize the risk of incurring significantly high costs. Specifically, w…
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
Chenyu Liu, Yan Zhang, Yi Shen +1
In this paper, we consider a transfer Reinforcement Learning (RL) problem in continuous state and action spaces, under unobserved contextual information. For example, the context c…