Composing Complex Skills by Learning Transition Policies

Youngwoon Lee*
Shao-Hua Sun*
Sriram Somasundaram
Edward S. Hu
Joseph J. Lim
University of Southern California
In ICLR 2019
[Paper]   [Code]




Humans acquire complex skills by exploiting previously learned skills and making transitions between them. To empower machines with this ability, we propose a method that can learn transition policies which effectively connect primitive skills to perform sequential tasks without handcrafted rewards. To efficiently train our transition policies, we introduce proximity predictors which induce rewards gauging proximity to suitable initial states for the next skill. The proposed method is evaluated on a set of complex continuous control tasks in bipedal locomotion and robotic arm manipulation which traditional policy gradient methods struggle at.



Highlights

This tough environment requires the agent to walk, jump and crawl its way to success.

Inspired by tennis, this task is composed of tossing and hitting a ball to a target.

Similar to a guard patrol, the agent must walk forwards and backwards repeatedly.



Training Curves



Manipulation



Locomotion



Citation

@inproceedings{lee2019composing,
  title = {Composing Complex Skills by Learning Transition Policies},
  author = {Lee, Youngwoon and Sun, Shao-Hua and Somasundaram, Sriram and Hu, Edward S and Lim, Joseph J},
  booktitle = {Proceedings of International Conference on Learning Representations},
  year = {2019},
  url={https://openreview.net/forum?id=rygrBhC5tQ},
}
        


Acknowledgements

This project was supported by the center for super intelligence, Kakao Brain, and SKT. The authors would like to thank Yuan-Hong Liao for helpful discussions during initial ideation.



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