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1701.04113
Cited By
Near Optimal Behavior via Approximate State Abstraction
15 January 2017
David Abel
D Ellis Hershkowitz
Michael L. Littman
OffRL
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Papers citing
"Near Optimal Behavior via Approximate State Abstraction"
37 / 87 papers shown
Title
Reinforcement Learning with Subspaces using Free Energy Paradigm
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Reshad Hosseini
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M. N. Ahmadabadi
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13 Dec 2020
Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning
Akshay Sharma
Piyush Rajesh Medikeri
Yu Zhang
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18 Nov 2020
Loss Bounds for Approximate Influence-Based Abstraction
E. Congeduti
A. Mey
F. Oliehoek
63
9
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03 Nov 2020
Approximate information state for approximate planning and reinforcement learning in partially observed systems
Jayakumar Subramanian
Amit Sinha
Raihan Seraj
Aditya Mahajan
155
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0
17 Oct 2020
Randomized Value Functions via Posterior State-Abstraction Sampling
Dilip Arumugam
Benjamin Van Roy
OffRL
85
7
0
05 Oct 2020
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Tom Silver
Rohan Chitnis
Aidan Curtis
J. Tenenbaum
Tomas Lozano-Perez
L. Kaelbling
GNN
93
80
0
11 Sep 2020
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Rohan Chitnis
Tom Silver
Beomjoon Kim
L. Kaelbling
Tomas Lozano-Perez
73
27
0
26 Jul 2020
Approximation Benefits of Policy Gradient Methods with Aggregated States
Daniel Russo
123
7
0
22 Jul 2020
Provably Safe PAC-MDP Exploration Using Analogies
Melrose Roderick
Vaishnavh Nagarajan
J. Zico Kolter
25
12
0
07 Jul 2020
Environment Shaping in Reinforcement Learning using State Abstraction
Parameswaran Kamalaruban
R. Devidze
Volkan Cevher
Adish Singla
OffRL
51
4
0
23 Jun 2020
ELSIM: End-to-end learning of reusable skills through intrinsic motivation
A. Aubret
L. Matignon
S. Hassas
52
5
0
23 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
R. Akrour
Davide Tateo
Jan Peters
60
22
0
10 Jun 2020
Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels
Arsham Mostaani
T. Vu
Symeon Chatzinotas
Björn E. Ottersten
67
11
0
28 May 2020
Learning Transferable Concepts in Deep Reinforcement Learning
Diego Gomez
Nicanor Quijano
Luis Felipe Giraldo
CLL
SSL
31
0
0
16 May 2020
TOMA: Topological Map Abstraction for Reinforcement Learning
Zhao-Heng Yin
Wu-Jun Li
37
2
0
11 May 2020
Exchangeable Input Representations for Reinforcement Learning
John Mern
Dorsa Sadigh
Mykel J. Kochenderfer
107
4
0
19 Mar 2020
Learning Discrete State Abstractions With Deep Variational Inference
Ondrej Biza
Robert Platt
Jan-Willem van de Meent
Lawson L. S. Wong
BDL
81
12
0
09 Mar 2020
Adaptive Temporal Difference Learning with Linear Function Approximation
Tao Sun
Han Shen
Tianyi Chen
Dongsheng Li
77
23
0
20 Feb 2020
Learning State Abstractions for Transfer in Continuous Control
Kavosh Asadi
David Abel
Michael L. Littman
OffRL
63
7
0
08 Feb 2020
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine
Weisi Guo
69
40
0
11 Nov 2019
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
Zhiyong Du
Yansha Deng
Weisi Guo
A. Nallanathan
Qi-hui Wu
34
30
0
11 Oct 2019
Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Mahsa Asadi
M. S. Talebi
Hippolyte Bourel
Odalric-Ambrym Maillard
OffRL
101
9
0
09 Oct 2019
Online Planning with Lookahead Policies
Yonathan Efroni
Mohammad Ghavamzadeh
Shie Mannor
48
5
0
10 Sep 2019
A Sufficient Statistic for Influence in Structured Multiagent Environments
F. Oliehoek
Stefan J. Witwicki
L. Kaelbling
81
23
0
22 Jul 2019
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada
Saurabh Kumar
Jacob Buckman
Ofir Nachum
Marc G. Bellemare
BDL
99
290
0
06 Jun 2019
Object Exchangeability in Reinforcement Learning: Extended Abstract
John Mern
Dorsa Sadigh
Mykel Kochenderfer
OCL
51
1
0
07 May 2019
From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots
Yu-an Zhang
53
1
0
01 May 2019
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen
Nan Jiang
OOD
OffRL
188
378
0
01 May 2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare
Will Dabney
Robert Dadashi
Adrien Ali Taïga
Pablo Samuel Castro
Nicolas Le Roux
Dale Schuurmans
Tor Lattimore
Clare Lyle
67
90
0
31 Jan 2019
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
Lucas Lehnert
Michael L. Littman
103
10
0
31 Jan 2019
Performance Guarantees for Homomorphisms Beyond Markov Decision Processes
Sultan Javed Majeed
Marcus Hutter
23
9
0
09 Nov 2018
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum
S. Gu
Honglak Lee
Sergey Levine
91
211
0
02 Oct 2018
Approximate Exploration through State Abstraction
Adrien Ali Taïga
Aaron Courville
Marc G. Bellemare
65
13
0
29 Aug 2018
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Iulian Serban
Chinnadhurai Sankar
Michael Pieper
Joelle Pineau
Yoshua Bengio
OffRL
96
29
0
12 Jul 2018
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
Vincent François-Lavet
Guillaume Rabusseau
Joelle Pineau
D. Ernst
R. Fonteneau
OffRL
83
34
0
22 Sep 2017
Unsupervised Basis Function Adaptation for Reinforcement Learning
Edward W. Barker
C. Ras
OffRL
47
3
0
03 Mar 2017
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
David Abel
J. Salvatier
Andreas Stuhlmuller
Owain Evans
88
62
0
15 Jan 2017
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