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Toward Interpretable Deep Reinforcement Learning with Linear Model
  U-Trees

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

16 July 2018
Guiliang Liu
Oliver Schulte
Wang Zhu
Qingcan Li
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees"

16 / 66 papers shown
Title
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
Zhaorong Li
Yang Yu
Yingfeng Chen
Ke Chen
Zhipeng Hu
Changjie Fan
38
5
0
16 Aug 2021
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Pulkit Verma
Shashank Rao Marpally
Siddharth Srivastava
ELMLLMAG
76
20
0
28 Jul 2021
Feature-Based Interpretable Reinforcement Learning based on
  State-Transition Models
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models
Omid Davoodi
Majid Komeili
FAttOffRL
61
6
0
14 May 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand
  Challenges
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaMLAI4CELRM
245
678
0
20 Mar 2021
RLTIR: Activity-based Interactive Person Identification based on
  Reinforcement Learning Tree
RLTIR: Activity-based Interactive Person Identification based on Reinforcement Learning Tree
Qingyang Li
Zhiwen Yu
L. Yao
Bin Guo
20
4
0
20 Mar 2021
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Zihan Ding
Pablo Hernandez-Leal
G. Ding
Changjian Li
Ruitong Huang
65
21
0
15 Nov 2020
Domain-Level Explainability -- A Challenge for Creating Trust in
  Superhuman AI Strategies
Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategies
Jonas Andrulis
Ole Meyer
Grégory Schott
Samuel Weinbach
V. Gruhn
39
4
0
12 Nov 2020
Designing Interpretable Approximations to Deep Reinforcement Learning
Designing Interpretable Approximations to Deep Reinforcement Learning
Nathan Dahlin
K. C. Kalagarla
Nikhil Naik
Rahul Jain
Pierluigi Nuzzo
59
9
0
28 Oct 2020
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear
  Decision Trees for Discrete Action Systems
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
Yashesh D. Dhebar
Kalyanmoy Deb
S. Nageshrao
Ling Zhu
Dimitar Filev
68
16
0
20 Sep 2020
TripleTree: A Versatile Interpretable Representation of Black Box Agents
  and their Environments
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
Tom Bewley
J. Lawry
FAtt
74
27
0
10 Sep 2020
Identifying Critical States by the Action-Based Variance of Expected
  Return
Identifying Critical States by the Action-Based Variance of Expected Return
Izumi Karino
Yoshiyuki Ohmura
Yasuo Kuniyoshi
OffRL
13
2
0
26 Aug 2020
Am I Building a White Box Agent or Interpreting a Black Box Agent?
Am I Building a White Box Agent or Interpreting a Black Box Agent?
Tom Bewley
27
1
0
02 Jul 2020
Automatic Discovery of Interpretable Planning Strategies
Automatic Discovery of Interpretable Planning Strategies
Julian Skirzyñski
Frederic Becker
Falk Lieder
90
15
0
24 May 2020
Explainable Reinforcement Learning: A Survey
Explainable Reinforcement Learning: A Survey
Erika Puiutta
Eric M. S. P. Veith
XAI
105
248
0
13 May 2020
Learning an Interpretable Traffic Signal Control Policy
Learning an Interpretable Traffic Signal Control Policy
James Ault
Josiah P. Hanna
Guni Sharon
45
50
0
23 Dec 2019
Interpretable Multi-Objective Reinforcement Learning through Policy
  Orchestration
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Ritesh Noothigattu
Djallel Bouneffouf
Nicholas Mattei
Rachita Chandra
Piyush Madan
Kush R. Varshney
Murray Campbell
Moninder Singh
F. Rossi
AI4CE
65
23
0
21 Sep 2018
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