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cs.LG2026
Computationally efficient safe exploration in reinforcement learning
Shreeram Murali, Shankar A. Deka, Dominik Baumann
Reinforcement learning in real-life applications requires safety guarantees during exploration. Typical reinforcement learning algorithms do not provide such guarantees, and many m…
eess.SY2026
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
Qingyun Guo, Junyi Shi, Tomasz Piotr Kucner +1
Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems. However, most existing methods rely…
cs.LG2026
Computationally lightweight classifiers with frequentist bounds on predictions
Shreeram Murali, Cristian R. Rojas, Dominik Baumann
While both classical and neural network classifiers can achieve high accuracy, they fall short on offering uncertainty bounds on their predictions, making them unfit for safety-cri…