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Found 6 papers, 4 papers with code
The Entrapment Problem in Random Walk Decentralized Learning
This paper explores decentralized learning in a graph-based setting, where data is distributed across nodes.
A DPLL(T) Framework for Verifying Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems.
Formal Language Constrained Markov Decision Processes
In order to satisfy safety conditions, an agent may be constrained from acting freely.
Deep Learning & Software Engineering: State of Research and Future Directions
The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.
Formal Language Constraints for Markov Decision Processes
In order to satisfy safety conditions, an agent may be constrained from acting freely.
Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning
We study the problem of deep reinforcement learning where the agent's action sequences are constrained, e. g., prohibition of dithering or overactuating action sequences that might damage a robot, drone, or other physical device.
