Date of Completion
Spring 5-10-2021
Thesis Advisor(s)
Yufeng Wu
Honors Major
Computer Science and Engineering
Disciplines
Artificial Intelligence and Robotics | Theory and Algorithms
Abstract
Reinforcement learning is a widely popular topic that has resulted in a plethora of
research papers and interest from academia and industry. When applied with robotics,
the field has showed some promising signs that robots can achieve levels of complex
cognitive abilities rivaling humans, but the goal of creating sapient robots is far from
a reality due to many challenges involved with training robots in a real world setting.
This paper will provide a survey regarding the keys towards realistic robotic training by
detailing the challenges and overviewing the reinforcement learning solutions involved
in getting a robot to think like a person.
Recommended Citation
Jaramillo, Andres, "Reinforcement Learning for Realistic Robotic Training: A Survey" (2021). Honors Scholar Theses. 1119.
https://digitalcommons.lib.uconn.edu/srhonors_theses/1119