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通过多环境方法加速流量控制的深度强化学习策略：流体物理学：第 31 卷，第 9 期
Source: aip.scitation.org
Article summary:
1. Deep Reinforcement Learning (DRL) has been proposed as a method for discovering complex active flow control strategies.
2. This work discusses a parallelization solution to accelerate the process of using DRL to research more complex fluid mechanics problems.
3. The solution involves parallelizing both the numerical simulation itself and adjusting the DRL algorithm for parallelization, resulting in perfect acceleration with batch sizes of DRL agents and slight under-extension for more simulations.
Article analysis:
The article is generally reliable and trustworthy, providing an overview of how Deep Reinforcement Learning (DRL) can be used to discover complex active flow control strategies, as well as discussing a parallelization solution to accelerate the process of using DRL to research more complex fluid mechanics problems. The article provides detailed information on the two methods used in this solution - parallelizing the numerical simulation itself and adjusting the DRL algorithm for parallelization - and provides evidence that this approach results in perfect acceleration with batch sizes of DRL agents and slight under-extension for more simulations.
The article does not appear to have any potential biases or one-sided reporting, nor does it contain any unsupported claims or missing points of consideration. All claims made are supported by evidence from previous studies, and all relevant points are discussed in detail. There is also no promotional content or partiality present in the article, nor are any possible risks noted. The article presents both sides equally, providing an unbiased overview of how Deep Reinforcement Learning can be used to discover complex active flow control strategies and how this process can be accelerated through a combination of two methods - parallelizing the numerical simulation itself and adjusting the DRL algorithm for parallelization.