EasyRL: A Simple and Extensible Reinforcement Learning Framework
August 04, 2020 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
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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
arXiv ID
2008.01700
Category
cs.AI: Artificial Intelligence
Citations
2
Venue
AAAI Conference on Artificial Intelligence
Last Checked
4 months ago
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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