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Found 7 papers, 1 papers with code
Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout
We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller.
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems
In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation.
Trust-aware Control for Intelligent Transportation Systems
Many intelligent transportation systems are multi-agent systems, i. e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as interacting agents.
Pareto Policy Adaptation
We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference
Our technique uses model simulations to learn {\em surrogate models}, and uses {\em conformal inference} to provide probabilistic guarantees on the satisfaction of a given STL property.
Automatic Testing With Reusable Adversarial Agents
We propose an interactive multi-agent framework where the system-under-design is modeled as an ego agent and its environment is modeled by a number of adversarial (ado) agents.
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
We propose using propositional logic to specify the importance of multiple objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
