---
title: "Combining Reinforcement Learning and Causal Models for Robotics Applications"
authors:
  - "Arquímides Méndez-Molina"
doi: "10.24963/ijcai.2021/684"
url: "https://ijcai.org/proceedings/2021/684"
pdf: "https://ijcai.org/proceedings/2021/0684.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4905-4906"
track: "Doctoral Consortium"
keywords:
  - "Uncertainty in AI: Graphical Models"
  - "Robotics: Learning in Robotics"
  - "Machine Learning: Reinforcement Learning"
---

# Combining Reinforcement Learning and Causal Models for Robotics Applications

Arquímides Méndez-Molina

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4905-4906. DOI: [10.24963/ijcai.2021/684](https://doi.org/10.24963/ijcai.2021/684)

## 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.