Rapidly exploring learning trees

@article{Shiarlis2017RapidlyEL,
  title={Rapidly exploring learning trees},
  author={Kyriacos Shiarlis and Jo{\~a}o V. Messias and Shimon Whiteson},
  journal={2017 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2017},
  pages={1541-1548}
}
Inverse Reinforcement Learning (IRL) for path planning enables robots to learn cost functions for difficult tasks from demonstration, instead of hard-coding them. However, IRL methods face practical limitations that stem from the need to repeat costly planning procedures. In this paper, we propose Rapidly Exploring Learning Trees (RLT∗), which learns the cost functions of Optimal Rapidly Exploring Random Trees (RRT∗) from demonstration, thereby making inverse learning methods applicable to more… CONTINUE READING

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