1 citations · 3 across the 9 of their papers we have counts for
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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…
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…
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…