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Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement
  Learning

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning

27 May 2019
Caleb Chuck
Supawit Chockchowwat
S. Niekum
ArXivPDFHTML

Papers citing "Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning"

5 / 5 papers shown
Title
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Caleb Chuck
Fan Feng
Carl Qi
Chang Shi
Siddhant Agarwal
Amy Zhang
S. Niekum
47
0
0
06 May 2025
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
Jesse Zhang
Haonan Yu
Wenyuan Xu
BDL
132
82
0
16 Jan 2021
A Review of Robot Learning for Manipulation: Challenges,
  Representations, and Algorithms
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer
S. Niekum
George Konidaris
36
356
0
06 Jul 2019
A Compositional Object-Based Approach to Learning Physical Dynamics
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang
T. Ullman
Antonio Torralba
J. Tenenbaum
AI4CE
OCL
241
438
0
01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
283
1,401
0
01 Dec 2016
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