JORLDY: a fully customizable open source framework for reinforcement learning
It addresses the need for customizable tools in the RL community, though it is incremental as it builds on existing frameworks and algorithms.
The paper tackles the lack of accessible reinforcement learning frameworks for researchers and students by proposing JORLDY, an open-source framework that provides over 20 RL algorithms and supports multiple environments, aiming to facilitate RL research.
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for researchers or students who want to study RL. In response, we propose an open-source RL framework "Join Our Reinforcement Learning framework for Developing Yours" (JORLDY). JORLDY provides more than 20 widely used RL algorithms which are implemented with Pytorch. Also, JORLDY supports multiple RL environments which include OpenAI gym, Unity ML-Agents, Mujoco, Super Mario Bros and Procgen. Moreover, the algorithmic components such as agent, network, environment can be freely customized, so that the users can easily modify and append algorithmic components. We expect that JORLDY will support various RL research and contribute further advance the field of RL. The source code of JORLDY is provided on the following Github: https://github.com/kakaoenterprise/JORLDY