Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
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 de...
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Main Authors | , , , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
03.09.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2109.01768 |