Search Results for author:
Found 8 papers, 2 papers with code
Deep Policy Optimization with Temporal Logic Constraints
In our work, we consider the setting where the task is specified by an LTL objective and there is an additional scalar reward that we need to optimize.
Learning Formal Specifications from Membership and Preference Queries
Active learning is a well-studied approach to learning formal specifications, such as automata.
Who Needs to Know? Minimal Knowledge for Optimal Coordination
We apply this algorithm to analyze the strategically relevant information for tasks in both a standard and a partially observable version of the Overcooked environment.
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning
Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving.
Demonstration Informed Specification Search
This paper considers the problem of learning temporal task specifications, e. g. automata and temporal logic, from expert demonstrations.
Learning Differentiable Programs with Admissible Neural Heuristics
This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.
Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs
We study the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.
