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Found 6 papers, 1 papers with code
Interpretable Generative Adversarial Imitation Learning
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations.
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula.
Time-Incremental Learning from Data Using Temporal Logics
In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal.
Learning Spatio-Temporal Specifications for Dynamical Systems
In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications.
Classification of Time-Series Data Using Boosted Decision Trees
Our algorithm leverages an ensemble of Concise Decision Trees (CDTs) to improve the classification performance, where each CDT is a decision tree that is empowered by a set of techniques to generate simpler formulae and improve interpretability.
Inferring Temporal Logic Properties from Data using Boosted Decision Trees
Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited data.
