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RUDDER: Return Decomposition for Delayed Rewards

RUDDER: Return Decomposition for Delayed Rewards

20 June 2018
Jose A. Arjona-Medina
Michael Gillhofer
Michael Widrich
Thomas Unterthiner
Johannes Brandstetter
Sepp Hochreiter
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Papers citing "RUDDER: Return Decomposition for Delayed Rewards"

27 / 27 papers shown
Title
Economic Battery Storage Dispatch with Deep Reinforcement Learning from Rule-Based Demonstrations
Economic Battery Storage Dispatch with Deep Reinforcement Learning from Rule-Based Demonstrations
Manuel Sage
Martin Staniszewski
Yaoyao Fiona Zhao
18
2
0
06 Apr 2025
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm
H. Kim
Kanghoon Lee
J. Park
Jiachen Li
Jinkyoo Park
60
1
0
05 Mar 2025
Evolution and The Knightian Blindspot of Machine Learning
Evolution and The Knightian Blindspot of Machine Learning
Joel Lehman
Elliot Meyerson
Tarek El-Gaaly
Kenneth O. Stanley
Tarin Ziyaee
81
1
0
22 Jan 2025
Instance Temperature Knowledge Distillation
Instance Temperature Knowledge Distillation
Zhengbo Zhang
Yuxi Zhou
Jia Gong
Jun Liu
Zhigang Tu
19
2
0
27 Jun 2024
Informativeness of Reward Functions in Reinforcement Learning
Informativeness of Reward Functions in Reinforcement Learning
R. Devidze
Parameswaran Kamalaruban
Adish Singla
11
2
0
10 Feb 2024
A User Study on Explainable Online Reinforcement Learning for Adaptive
  Systems
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems
Andreas Metzger
Jan Laufer
Felix Feit
Klaus Pohl
OffRL
OnRL
8
1
0
09 Jul 2023
Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation
Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation
Wenyan Yang
A. Angleraud
R. Pieters
J. Pajarinen
Joni-Kristian Kämäräinen
19
6
0
05 Mar 2023
Preference Transformer: Modeling Human Preferences using Transformers
  for RL
Preference Transformer: Modeling Human Preferences using Transformers for RL
Changyeon Kim
Jongjin Park
Jinwoo Shin
Honglak Lee
Pieter Abbeel
Kimin Lee
OffRL
25
61
0
02 Mar 2023
Feature construction using explanations of individual predictions
Feature construction using explanations of individual predictions
Boštjan Vouk
Matej Guid
Marko Robnik-Šikonja
FAtt
16
10
0
23 Jan 2023
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer
  Value Function
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Clément Bonnet
Laurence Midgley
Alexandre Laterre
16
1
0
19 Nov 2022
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement
  Learning
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning
Jennifer She
Jayesh K. Gupta
Mykel J. Kochenderfer
16
4
0
31 Oct 2022
A Globally Convergent Evolutionary Strategy for Stochastic Constrained
  Optimization with Applications to Reinforcement Learning
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning
Youssef Diouane
Aurélien Lucchi
Vihang Patil
11
3
0
21 Feb 2022
Selective Credit Assignment
Selective Credit Assignment
Veronica Chelu
Diana Borsa
Doina Precup
Hado van Hasselt
17
2
0
20 Feb 2022
Retrieval-Augmented Reinforcement Learning
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal
A. Friesen
Andrea Banino
T. Weber
Nan Rosemary Ke
...
Michal Valko
Simon Osindero
Timothy Lillicrap
N. Heess
Charles Blundell
OffRL
17
53
0
17 Feb 2022
Bayesian sense of time in biological and artificial brains
Bayesian sense of time in biological and artificial brains
Z. Fountas
Alexey Zakharov
11
0
0
14 Jan 2022
Evaluating the progress of Deep Reinforcement Learning in the real
  world: aligning domain-agnostic and domain-specific research
Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
J. Luis
E. Crawley
B. Cameron
OffRL
18
6
0
07 Jul 2021
Towards Practical Credit Assignment for Deep Reinforcement Learning
Towards Practical Credit Assignment for Deep Reinforcement Learning
Vyacheslav Alipov
Riley Simmons-Edler
N.Yu. Putintsev
Pavel Kalinin
Dmitry Vetrov
OffRL
25
11
0
08 Jun 2021
A Survey on Deep Reinforcement Learning for Audio-Based Applications
A Survey on Deep Reinforcement Learning for Audio-Based Applications
S. Latif
Heriberto Cuayáhuitl
Farrukh Pervez
Fahad Shamshad
Hafiz Shehbaz Ali
Erik Cambria
OffRL
32
73
0
01 Jan 2021
Agent57: Outperforming the Atari Human Benchmark
Agent57: Outperforming the Atari Human Benchmark
Adria Puigdomenech Badia
Bilal Piot
Steven Kapturowski
Pablo Sprechmann
Alex Vitvitskyi
Daniel Guo
Charles Blundell
OffRL
11
506
0
30 Mar 2020
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang
Jianye Hao
Guangyong Chen
Hongyao Tang
Yingfeng Chen
Yujing Hu
Changjie Fan
Zhongyu Wei
11
52
0
10 Feb 2020
Towards Explainable Artificial Intelligence
Towards Explainable Artificial Intelligence
Wojciech Samek
K. Müller
XAI
14
433
0
26 Sep 2019
Explaining and Interpreting LSTMs
Explaining and Interpreting LSTMs
L. Arras
Jose A. Arjona-Medina
Michael Widrich
G. Montavon
Michael Gillhofer
K. Müller
Sepp Hochreiter
Wojciech Samek
FAtt
AI4TS
16
78
0
25 Sep 2019
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Tom Schaul
Diana Borsa
Joseph Modayil
Razvan Pascanu
11
63
0
25 Apr 2019
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy
  Critics
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Denis Steckelmacher
Hélène Plisnier
D. Roijers
A. Nowé
OffRL
13
17
0
11 Mar 2019
A Survey and Critique of Multiagent Deep Reinforcement Learning
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal
Bilal Kartal
Matthew E. Taylor
OffRL
14
548
0
12 Oct 2018
Sample-Efficient Deep Reinforcement Learning via Episodic Backward
  Update
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
Su Young Lee
Sung-Ik Choi
Sae-Young Chung
BDL
11
73
0
31 May 2018
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,233
0
24 Jun 2017
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