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The practical application of learning agents requires sample efficient and interpretable algorithms.
“A survey of monte carlo tree search methods”
Cameron Browne et al · 2012
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
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“Imitation Learning: A Survey of Learning Methods”
Ahmed Hussein, Mohamed Gaber, Eyad Elyan and Chrisina Jayne · 2017
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“An approach to hierarchical deep reinforcement learning for a decentralized walking control architecture”
Malte Schilling and Andrew Melnik · 2018
Earlier work this paper cites.
“Deep q-learning from demonstrations”
Todd Hester et al · 2018
Earlier work this paper cites.
“Biologically-inspired deep reinforcement learning of modular control for a six-legged robot”
Kai Konen, Timo Korthals, Andrew Melnik and Malte Schilling · 2019
Earlier work this paper cites.
“Recent advances in imitation learning from observation”
Faraz Torabi, Garrett Warnell and Peter Stone · 2019
Cited alongside, same era.
“Modularization of end-to-end learning: Case study in arcade games”
Andrew Melnik, Sascha Fleer, Malte Schilling and Helge Ritter · 2019
Cited alongside, same era.
“Learn to move through a combination of policy gradient algorithms: Ddpg, d4pg, and td3”
Nicolas Bach et al · 2020
Cited alongside, same era.
“Solving physics puzzles by reasoning about paths”
Augustin Harter et al · 2020
Cited alongside, same era.
“An error-based addressing architecture for dynamic model learning”
Nicolas Bach, Andrew Melnik, Federico Rosetto and Helge Ritter · 2020
Cited alongside, same era.
“Learn-to-Race: A Multimodal Control Environment for Autonomous Racing”, 2021
James Herman et al · 2021
Later among the works it cites.
“Critic guided segmentation of rewarding objects in first-person views”
Andrew Melnik et al · 2021
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Malte Schilling et al · 2021
Later among the works it cites.
“Learn-to-Race Autonomous Racing Virtual Challenge” https://www.aicrowd.com/challenges/learn-to-race-autonomous-racing-virtual-challenge ; accessed May 14, 2022
2022
Closest in time.
“Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space”
Shivansh Beohar et al · 2022
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“Simple and Effective VAE Training with Calibrated Decoders”, 2020
Oleh Rybkin, Kostas Daniilidis and Sergey Levine · 2020
Cited alongside, same era.