EasyRL: A Simple and Extensible Reinforcement Learning Framework
This work addresses the barrier to entry for newcomers to reinforcement learning by providing a more accessible tool, though it is incremental as it builds on existing frameworks like OpenAI Gym and KerasRL.
The authors tackled the complexity of using existing reinforcement learning frameworks by introducing EasyRL, a simpler framework with an interactive graphical user interface that allows users to train and evaluate RL agents without programming knowledge, while also supporting custom agents and environments for researchers.
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.