EasyRL: A Simple and Extensible Reinforcement Learning Framework

• URL: http://arxiv.org/abs/2008.01700v2
• Date: Thu, 5 Nov 2020 20:35:33 GMT
• Title: EasyRL: A Simple and Extensible Reinforcement Learning Framework
• Authors: Neil Hulbert, Sam Spillers, Brandon Francis, James Haines-Temons, Ken Gil Romero, Benjamin De Jager, Sam Wong, Kevin Flora, Bowei Huang, Athirai A. Irissappane
• Abstract summary: EasyRL provides an interactive graphical user interface for users to train and evaluate RL agents. 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.
• Score: 3.2173369911280023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In 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.

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