B Zhang, X Qiu, X Tan - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Reinforcement learning (RL) is increasingly applied in recommending ventilator parameters, yet existing methods prioritize therapeutic effect over patient safety. This leads to excessive …
Racing demands each vehicle to drive at its physical limits, when any safety infraction could lead to catastrophic failure. In this work, we study the problem of safe reinforcement learning …
Blame attribution is one of the key aspects of accountable decision making, as it provides means to quantify the responsibility of an agent for a decision making outcome. In this paper …
Offline reinforcement learning (RL) leverages previously collected data for policy optimization without any further active exploration. Despite the recent interest in this …
We show that the popular reinforcement learning (RL) strategy of estimating the stateaction value (Q-function) by minimizing the mean squared Bellman error leads to a regression …
S Tang, J Wiens - Advances in Neural Information …, 2023 - proceedings.neurips.cc
In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative evaluation using observational data can help practitioners understand the generalization …
This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games which are considered as one of the most popular online …
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by …
XK Jin, XH Liu, S Jiang, Y Yu - arXiv preprint arXiv:2206.02000, 2022 - arxiv.org
Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value …