Search Results for author:
Found 7 papers, 0 papers with code
Scaling Learning based Policy Optimization for Temporal Tasks via Dropout
We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives.
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
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
