mlsanityData to 7 Oct 2026

Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms

arXiv 2109.01768 ↗First posted 4 Sep 2021cs.LG cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 50 of its 63 references that are in the map. First posted on arXiv 4 Sep 2021.

Builds on

50 of 63 references.

References · 50

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023A Survey on Causal Reinforcement Learningcited by 43
  2. 2022Causality-driven Hierarchical Structure Discovery for Reinforcement Learningcited by 23