Fetching the paper…
Reading the bibliography…
Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
Hierarchical reinforcement learning for robot navigation
Bastian Bischoff, Duy Nguyen-Tuong, IH Lee, Felix Streichert, Alois Knoll, et al · 2013
Earlier work this paper cites.
Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver · 2016
Earlier work this paper cites.
Terrain-adaptive locomotion skills using deep reinforcement learning
Xue Bin Peng, Glen Berseth, and Michiel Van de Panne · 2016
Earlier work this paper cites.
Meta learning shared hierarchies
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman · 2017
Earlier work this paper cites.
Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning
Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne · 2017
Earlier work this paper cites.
Policies modulating trajectory generators
Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, and Vincent Vanhoucke · 2018
Cited alongside, same era.
Policy transfer with strategy optimization
Wenhao Yu, C Karen Liu, and Greg Turk · 2018
Cited alongside, same era.
Hierarchical reinforcement learning with hindsight
Andrew Levy, Robert Platt, and Kate Saenko · 2018
Cited alongside, same era.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine · 2018
Cited alongside, same era.
Latent space policies for hierarchical reinforcement learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
PRM-RL: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning
Aleksandra Faust, Kenneth Oslund, Oscar Ramirez, Anthony Francis, Lydia Tapia, Marek Fiser, and James Davidson · 2018
Later among the works it cites.
Simple random search provides a competitive approach to reinforcement learning
Horia Mania, Aurelia Guy, and Benjamin Recht · 2018
Later among the works it cites.
Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
Later among the works it cites.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Closest in time.
Hierarchical reinforcement learning via advantage-weighted information maximization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bullet Physics SDK
Erwin Coumans
Cited in the paper.
Takayuki Osa, Voot Tangkaratt, and Masashi Sugiyama · 2019
Closest in time.