Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
Ruizhi Chen, Xiaoyu Wu, Yansong Pan, Kaizhao Yuan, Ling Li, TianYun, Ma, JiYuan Liang, Rui Zhang, Kai Wang, Chen Zhang, Shaohui Peng, Xishan, Zhang, Zidong Du, Qi Guo, Yunji Chen

TL;DR
This paper introduces Eden, a unified, configurable virtual environment framework designed to evaluate and compare state-of-the-art reinforcement learning algorithms across diverse tasks, addressing limitations of existing environments.
Contribution
Eden provides a single, flexible environment framework for all major RL algorithms, enabling standardized evaluation and fostering development of general AI capabilities.
Findings
Supports all SOTA RL algorithms within one environment
Allows combined environments for multiple RL algorithm classes
Establishes a unified evaluation standard for RL algorithms
Abstract
With AlphaGo defeats top human players, reinforcement learning(RL) algorithms have gradually become the code-base of building stronger artificial intelligence(AI). The RL algorithm design firstly needs to adapt to the specific environment, so the designed environment guides the rapid and profound development of RL algorithms. However, the existing environments, which can be divided into real world games and customized toy environments, have obvious shortcomings. For real world games, it is designed for human entertainment, and too much difficult for most of RL researchers. For customized toy environments, there is no widely accepted unified evaluation standard for all RL algorithms. Therefore, we introduce the first virtual user-friendly environment framework for RL. In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.…
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Taxonomy
TopicsReinforcement Learning in Robotics · Advanced Bandit Algorithms Research · Evolutionary Algorithms and Applications
