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Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning

24 May 2025
Chi Zhang
Ziying Jia
George Atia
Sihong He
Yue Wang
ArXivPDFHTML

Papers citing "Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning"

15 / 65 papers shown
Title
Personalized Federated Learning: A Meta-Learning Approach
Personalized Federated Learning: A Meta-Learning Approach
Alireza Fallah
Aryan Mokhtari
Asuman Ozdaglar
FedML
139
565
0
19 Feb 2020
Federated Learning with Personalization Layers
Federated Learning with Personalization Layers
Manoj Ghuhan Arivazhagan
V. Aggarwal
Aaditya Kumar Singh
Sunav Choudhary
FedML
64
826
0
02 Dec 2019
DisCoRL: Continual Reinforcement Learning via Policy Distillation
DisCoRL: Continual Reinforcement Learning via Policy Distillation
Kalifou René Traoré
Hugo Caselles-Dupré
Timothée Lesort
Te Sun
Guanghang Cai
Natalia Díaz Rodríguez
David Filliat
OffRL
45
61
0
11 Jul 2019
Continual Reinforcement Learning deployed in Real-life using Policy
  Distillation and Sim2Real Transfer
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
Kalifou René Traoré
Hugo Caselles-Dupré
Timothée Lesort
Te Sun
Natalia Díaz Rodríguez
David Filliat
CLL
OffRL
91
45
0
11 Jun 2019
Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state
  Simulation, Quantiles, and Other Applications
Unbiased Multilevel Monte Carlo: Stochastic Optimization, Steady-state Simulation, Quantiles, and Other Applications
Jose H. Blanchet
Peter Glynn
Yanan Pei
29
29
0
22 Apr 2019
Active Domain Randomization
Active Domain Randomization
Bhairav Mehta
Manfred Diaz
Florian Golemo
C. Pal
Liam Paull
38
262
0
09 Apr 2019
How to pick the domain randomization parameters for sim-to-real transfer
  of reinforcement learning policies?
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Q. Vuong
Sharad Vikram
H. Su
Sicun Gao
Henrik I. Christensen
OOD
32
48
0
28 Mar 2019
Action Robust Reinforcement Learning and Applications in Continuous
  Control
Action Robust Reinforcement Learning and Applications in Continuous Control
Chen Tessler
Yonathan Efroni
Shie Mannor
52
234
0
26 Jan 2019
Markov Decision Processes with Continuous Side Information
Markov Decision Processes with Continuous Side Information
Aditya Modi
Nan Jiang
Satinder Singh
Ambuj Tewari
OffRL
41
61
0
15 Nov 2017
Distral: Robust Multitask Reinforcement Learning
Distral: Robust Multitask Reinforcement Learning
Yee Whye Teh
V. Bapst
Wojciech M. Czarnecki
John Quan
J. Kirkpatrick
R. Hadsell
N. Heess
Razvan Pascanu
120
551
0
13 Jul 2017
Domain Randomization for Transferring Deep Neural Networks from
  Simulation to the Real World
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Joshua Tobin
Rachel Fong
Alex Ray
Jonas Schneider
Wojciech Zaremba
Pieter Abbeel
171
2,948
0
20 Mar 2017
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
234
17,328
0
17 Feb 2016
Policy Distillation
Policy Distillation
Andrei A. Rusu
Sergio Gomez Colmenarejo
Çağlar Gülçehre
Guillaume Desjardins
J. Kirkpatrick
Razvan Pascanu
Volodymyr Mnih
Koray Kavukcuoglu
R. Hadsell
53
687
0
19 Nov 2015
Contextual Markov Decision Processes
Contextual Markov Decision Processes
Assaf Hallak
Dotan Di Castro
Shie Mannor
68
243
0
08 Feb 2015
Playing Atari with Deep Reinforcement Learning
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih
Koray Kavukcuoglu
David Silver
Alex Graves
Ioannis Antonoglou
Daan Wierstra
Martin Riedmiller
103
12,163
0
19 Dec 2013
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