137 citations · 525 across the 67 of their papers we have counts for
5 papers · 1 filter
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1
Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging whe…
Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks +4
The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on…
Rule-Guided Reinforcement Learning Policy Evaluation and Improvement
Martin Tappler, Ignacio D. Lopez-Miguel, Sebastian Tschiatschek +1
We consider the challenging problem of using domain knowledge to improve deep reinforcement learning policies. To this end, we propose LEGIBLE, a novel approach, following a multi-…
Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs
Andrey Kofnov, Daniel Kapla, Ezio Bartocci +1
We derive exact upper and lower bounds for the cumulative distribution function (cdf) of the output of a neural network (NN) over its entire support subject to noisy (stochastic) i…
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
Meiyi Ma, John Stankovic, Ezio Bartocci +1
Predictive monitoring -- making predictions about future states and monitoring if the predicted states satisfy requirements -- offers a promising paradigm in supporting the decisio…