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cs.LG2026
Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments
Qinwei Huang, Rui Zuo, Simon Khan +1
Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generated local data. However, in fe…
cs.LG2025
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
Zilong Deng, Simon Khan, Shaofeng Zou
In this work, we study the sample complexity problem of risk-sensitive Reinforcement Learning (RL) with a generative model, where we aim to maximize the Conditional Value at Risk (…
cs.LG2024
Criticality and Safety Margins for Reinforcement Learning
Alexander Grushin, Walt Woods, Alvaro Velasquez +1
State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during…