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Exploration in Approximate Hyper-State Space for Meta Reinforcement
  Learning

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

2 October 2020
L. Zintgraf
Leo Feng
Cong Lu
Maximilian Igl
Kristian Hartikainen
Katja Hofmann
Shimon Whiteson
ArXivPDFHTML

Papers citing "Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning"

6 / 6 papers shown
Title
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting
  Diversity
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
Robby Costales
Stefanos Nikolaidis
AI4CE
31
0
0
07 Nov 2024
Bridging State and History Representations: Understanding
  Self-Predictive RL
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni
Benjamin Eysenbach
Erfan Seyedsalehi
Michel Ma
Clement Gehring
Aditya Mahajan
Pierre-Luc Bacon
AI4TS
AI4CE
22
20
0
17 Jan 2024
A Survey of Meta-Reinforcement Learning
A Survey of Meta-Reinforcement Learning
Jacob Beck
Risto Vuorio
E. Liu
Zheng Xiong
L. Zintgraf
Chelsea Finn
Shimon Whiteson
OOD
OffRL
37
122
0
19 Jan 2023
Planning to the Information Horizon of BAMDPs via Epistemic State
  Abstraction
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
Dilip Arumugam
Satinder Singh
24
3
0
30 Oct 2022
Distributionally Adaptive Meta Reinforcement Learning
Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay
Abhishek Gupta
Dibya Ghosh
Sergey Levine
Pulkit Agrawal
OOD
29
14
0
06 Oct 2022
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
338
11,684
0
09 Mar 2017
1