Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
Abhishek Gupta Justin Yu Tony Z. Zhao Vikash Kumar Aaron Rovinsky Kelvin Xu Thomas Devlin Sergey Levine Abhishek Gupta Justin Yu Tony Z. Zhao Vikash Kumar Aaron Rovinsky Kelvin Xu Thomas Devlin Sergey Levine
Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error. However, most RL algorithms use a carefully engineered setup in order to collect data, requiring human supervision and intervention to provide episodic resets. This is particularly evident in challenging robotics problems, s...