Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies

@article{Esteban2019HierarchicalRL,
  title={Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies},
  author={Domingo Esteban and Leonel Dario Rozo and Darwin G. Caldwell},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  pages={1818-1825}
}
  • Domingo Esteban, Leonel Dario Rozo, Darwin G. Caldwell
  • Published 2019
  • Computer Science, Engineering
  • 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore, all these individual (composable) policies need to be learned before tackling the learning process of the complex task through policies composition… CONTINUE READING

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