Sundaram, R., & Lubina, B. (2022, August), Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40590

\bibitem{asee_peer_40590}
  Sundaram, R., \& Lubina, B. (2022, August), \emph{Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--40590

Ramakrishnan Sundaram and Benjamin Lubina.  "Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/40590 Internet.  23 Apr, 2024

\bibitem{asee_peer_40590}
  Ramakrishnan Sundaram and Benjamin Lubina.
  "Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/40590 Internet.  23 Apr, 2024

@INPROCEEDINGS{asee_peer_40590
author = "Ramakrishnan Sundaram and Benjamin Lubina"
title = "Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/40590}
number = {10.18260/1-2--40590}
}

TY  - CPAPER
AB  - This paper describes the setup of a reinforcement learning project that is meant to support student research and curriculum development into the upcoming fields of machine learning and artificial intelligence. Reinforcement learning in terms of this paper consists of appropriate setup of input features and output results, configuration of the architecture of the network, appropriate reward shaping, and proper sampling of generations and testing advancements with proper analytics. The hardware requirements of this project are met with the university’s cost-effective workstation platform and more applicable platforms are currently planned to come into the university’s possession to realistically be applied in a classroom setting. Software requirements are all free and able to be used in a classroom setting, the specifics of which will be elaborated upon. The project is made up of modules for self-learning the use of software in an attempt to play it as best as it can. In this case, to increase engagement and therefore best educate students on the concepts and practice of machine learning. The software of choice will be games simple enough for an introductory student to develop a reinforcement learning model for. These video game(s) will be used as a way to pique interest and therefore increase engagement in the material. In addition to serving as an introductory foray into machine learning for students, the project can also serve as a platform or reference for student research and more advanced projects to be built on. Graduate courses in machine learning and late undergraduate software classes would be good choices as ECE courses have previously had Python experience, which is needed to create this project in a classroom setting.
AU  - Ramakrishnan Sundaram
AU  - Benjamin Lubina
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Work-in-Progress: Introductory Reinforcement Learning for Student Education and Curriculum Development Through Engaging Mediums
UR  - https://peer.asee.org/40590
DO  - 10.18260/1-2--40590
ER  -