EasyRL: A Simple and Extensible Reinforcement Learning Framework
Keywords:RL Framework, Deep Reinforcement Learning
AbstractIn recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To this end, many researchers have built RL frameworks such as openAI Gym, and KerasRL for ease of use. While these works have made great strides towards bringing down the barrier of entry for those new to RL, we propose a much simpler framework called EasyRL, by providing an interactive graphical user interface for users to train and evaluate RL agents. As it is entirely graphical, EasyRL does not require programming knowledge for training and testing simple built-in RL agents. EasyRL also supports custom RL agents and environments, which can be highly beneficial for RL researchers in evaluating and comparing their RL models.
How to Cite
Hulbert, N., Spillers, S., Francis, B., Haines-Temons, J., Gil Romero, K., De Jager, B., Wong, S., Flora, K., Huang, B., & A. Irissappane, A. (2021). EasyRL: A Simple and Extensible Reinforcement Learning Framework. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16041-16043. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18006
AAAI Demonstration Track