Combining Reinforcement Learning and Causal Models for Robotics Applications

Arquímides Méndez-Molina

IJCAI 2021poster7 citations

Combining Reinforcement Learning and Causal Models for Robotics Applications

Abstract

The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to improve their respective learning processes, especially in the context of our application area (service robotics). The preliminary results obtained so far are a good starting point for thinking about the success of our research project.

Machine Learning: Reinforcement LearningUncertainty in AI: Graphical ModelsRobotics: Learning in Robotics

BibTeX

@inproceedings{ijcai2021p684,
  title     = {Combining Reinforcement Learning and Causal Models for Robotics Applications},
  author    = {Méndez-Molina, Arquímides},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4905--4906},
  year      = {2021},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2021/684},
  url       = {https://doi.org/10.24963/ijcai.2021/684},
}