Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms

• URL: http://arxiv.org/abs/2109.01768v1
• Date: Sat, 4 Sep 2021 02:38:08 GMT
• Title: Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
• Authors: 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
• Abstract summary: reinforcement learning algorithms have gradually become the code-base of building stronger artificial intelligence(AI) Existing environments, which can be divided into real world games and customized toy environments, have obvious shortcomings. We introduce the first virtual user-friendly environment framework for RL.
• Score: 19.62620266334838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Then all the mainstream state-of-the-art(SOTA) RL algorithms can be conveniently evaluated and compared. Therefore, our contributions mainly includes the following aspects: 1.single configured environment for all classification of SOTA RL algorithms; 2.combined environment of more than one classification RL algorithms; 3.the evaluation standard for all kinds of RL algorithms. With all these efforts, a possibility for breeding an AI with capability of general competency in a variety of tasks is provided, and maybe it will open up a new chapter for AI.

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