论文标题
有效的加强学习，可以通过部分可观察到流体流量控制
Efficient reinforcement learning with partially observable for fluid flow control
论文作者
论文摘要
尽管耗散粘性液的尺寸较低，但加固学习（RL）仍需要许多可观察到的流体控制问题。这是因为假定可观察到在RL框架中遵循独立于政策的马尔可夫决策过程。通过将策略参数作为价值函数的参数，我们构建了一种具有部分可观察情况的一致算法。使用典型的活动流控制示例，我们表明我们的算法比现有的RL算法更稳定，更有效，即使在少量可观察到的情况下也是如此。
Despite the low dimensionalities of dissipative viscous fluids, reinforcement learning (RL) requires many observables in fluid control problems. This is because the observables are assumed to follow a policy-independent Markov decision process in the RL framework. By including policy parameters as arguments of a value function, we construct a consistent algorithm with partially observable condition. Using typical examples of active flow control, we show that our algorithm is more stable and efficient than the existing RL algorithms, even under a small number of observables.
