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Found 3 papers, 2 papers with code
Learning Environment Models with Continuous Stochastic Dynamics
We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent.
On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth
We examine the assumption that the hidden-state vectors of recurrent neural networks (RNNs) tend to form clusters of semantically similar vectors, which we dub the clustering hypothesis.
Automata Learning meets Shielding
Iteratively, we use the collected data to learn new MDPs with higher accuracy, resulting in turn in shields able to prevent more safety violations.
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