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We argue that hierarchical methods can become the key for modular robots achieving reconfigurability.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard Sutton, Doina Precup, and Satinder Singh · 1999
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2016
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Probabilistic inference for determining options in reinforcement learning
Christian Daniel, Herke van Hoof, Jan Peters, and Gerhard Neumann · 2016
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Strategic attentive writer for learning macro-actions
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Extending the openai gym for robotics: a toolkit for reinforcement learning using ros and gazebo
Iker Zamora, Nestor Gonzalez Lopez, Victor Mayoral Vilches, and Alejandro Hernandez Cordero · 2016
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Stochastic neural networks for hierarchical reinforcement learning
Carlos Florensa, Yan Duan, and Pieter Abbeel · 2017
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Meta learning shared hierarchies
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman · 2017
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The shift in the robotics paradigm; the hardware robot operating system (h-ros); an infrastructure to create interoperable robot components
V. Mayoral, A. Hernández, R. Kojcev, I. Muguruza, I. Zamalloa, A. Bilbao, and L. Usategi · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, and Jimmy Ba · 2017
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