108 citations · 110 across the 6 of their papers we have counts for
6 papers
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
Pedro Sequeira, Melinda Gervasio
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-d…
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
Pedro Sequeira, Jesse Hostetler, Melinda Gervasio
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-d…
A Framework for Understanding and Visualizing Strategies of RL Agents
Pedro Sequeira, Daniel Elenius, Jesse Hostetler +1
Recent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics i…
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
Eric Yeh, Pedro Sequeira, Jesse Hostetler +1
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characteriz…
Confidence Calibration for Domain Generalization under Covariate Shift
Yunye Gong, Xiao Lin, Yi Yao +3
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they req…
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations
Pedro Sequeira, Melinda Gervasio
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help…