5 papers
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…
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…
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…
Safety and optimality in learning-based control at low computational cost
Dominik Baumann, Krzysztof Kowalczyk, Cristian R. Rojas +2
Applying machine learning methods to physical systems that are supposed to act in the real world requires providing safety guarantees. However, methods that include such guarantees…
A computationally lightweight safe learning algorithm
Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels +1
Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response…