Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
X Liu, W Chen, M Tan - arXiv preprint arXiv:2309.06097, 2023 - arxiv.org
Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential
decision-making problems. However, existing DRL agents make decisions in an opaque …
decision-making problems. However, existing DRL agents make decisions in an opaque …
Explaining a deep reinforcement learning agent using regression trees
J Løver - 2021 - ntnuopen.ntnu.no
Bruken av" svart-boks"-modeller innen maskinlæring skaper problemer for systemer med
fokus på sikkerhet. Systemer som nyttegjør seg av dyp forsterkende læring (engelsk: deep …
fokus på sikkerhet. Systemer som nyttegjør seg av dyp forsterkende læring (engelsk: deep …
[PDF][PDF] Formal Methods use for Learning Assurance (ForMuLA)
EAIT Force - 2023 - easa.europa.eu
Executive summary The aim of this report is to present the outcome of the collaboration
between EASA and Collins Aerospace on an Innovation Partnership Contract (IPC) that …
between EASA and Collins Aerospace on an Innovation Partnership Contract (IPC) that …
[PDF][PDF] Tree Models for Interpretable Agents
T Bewley, T Bewley - AI (expert), 2012 - research-information.bris.ac.uk
As progress in AI impacts all sectors of society, the world is destined to see increasingly
complex and numerous autonomous decision-making agents, which act upon their …
complex and numerous autonomous decision-making agents, which act upon their …