Open-Ended Learning Strategies for Learning Complex Locomotion Skills

14 June 2022

Papers citing "Open-Ended Learning Strategies for Learning Complex Locomotion Skills"

8 / 8 papers shown

Title
CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion Ying-Sheng Luo Jonathan Hans Soeseno Trista Pei-chun Chen Wei-Chao Chen 56 15 0 07 May 2020
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions Rui Wang Joel Lehman Aditya Rawal Jiale Zhi Yulun Li Jeff Clune Kenneth O. Stanley 84 129 0 19 Mar 2020
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning Tianyu Li Nathan Lambert Roberto Calandra Franziska Meier Akshara Rai 73 40 0 26 Sep 2019
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Tuomas Haarnoja Aurick Zhou Pieter Abbeel Sergey Levine 317 8,406 0 04 Jan 2018
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents Edoardo Conti Vashisht Madhavan F. Such Joel Lehman Kenneth O. Stanley Jeff Clune 80 348 0 18 Dec 2017
Emergence of Locomotion Behaviours in Rich Environments N. Heess TB Dhruva S. Sriram Jay Lemmon J. Merel ... Tom Erez Ziyun Wang S. M. Ali Eslami Martin Riedmiller David Silver 202 938 0 07 Jul 2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning Tim Salimans Jonathan Ho Xi Chen Szymon Sidor Ilya Sutskever 115 1,544 0 10 Mar 2017
OpenAI Gym Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang Wojciech Zaremba OffRL ODL 223 5,086 0 05 Jun 2016