Combining Reinforcement Learning and Causal Models for Robotics Applications
Combining Reinforcement Learning and Causal Models for Robotics Applications
Arquímides Méndez-Molina
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 4905-4906.
https://doi.org/10.24963/ijcai.2021/684
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.
Keywords:
Machine Learning: Reinforcement Learning
Uncertainty in AI: Graphical Models
Robotics: Learning in Robotics