Combining Reinforcement Learning and Causal Models for Robotics Applications

Méndez-Molina, Arquímides

doi:10.24963/IJCAI.2021/684

Combining Reinforcement Learning and Causal Models for Robotics Applications

Arquímides Méndez-Molina

IJCAI 2021 pp. 4905-4906

doi:10.24963/IJCAI.2021/684 /ijcai/2021/mendezmolina2021ijcai-combining/

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.

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Cite

Text

Méndez-Molina. "Combining Reinforcement Learning and Causal Models for Robotics Applications." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/684

Markdown

[Méndez-Molina. "Combining Reinforcement Learning and Causal Models for Robotics Applications." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/mendezmolina2021ijcai-combining/) doi:10.24963/IJCAI.2021/684

BibTeX

@inproceedings{mendezmolina2021ijcai-combining,
  title     = {{Combining Reinforcement Learning and Causal Models for Robotics Applications}},
  author    = {Méndez-Molina, Arquímides},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {4905-4906},
  doi       = {10.24963/IJCAI.2021/684},
  url       = {https://mlanthology.org/ijcai/2021/mendezmolina2021ijcai-combining/}
}