research
∙
07/18/2023
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
In recent years, advances in deep learning have resulted in a plethora o...
share
research
∙
02/20/2023
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning
We approach the problem of understanding how people interact with each o...
research
∙
11/11/2022
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
In recent years, advances in deep learning have resulted in a plethora o...
research
∙
11/03/2022
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
We present an approach for autonomous sensor control for information gat...
research
∙
08/17/2022
A Framework for Understanding and Visualizing Strategies of RL Agents
Recent years have seen significant advances in explainable AI as the nee...
research
∙
07/15/2022
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible ...
research
∙
01/12/2022
Decomposition of admissible functions in weighted coupled cell networks
This work makes explicit the degrees of freedom involved in modeling the...
research
∙
12/19/2019
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations
We propose an explainable reinforcement learning (XRL) framework that an...
research
∙
02/05/2019