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A proactive decision support method based on deep reinforcement learning and state partition

机译：基于深度强化学习和状态划分的主动决策支持方法

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Big streaming data is an important kind of big data which we need new technology to process. Getting knowledge from online streaming data and making decision online can help us get more value from Big data. A proactive decision support system can predict future states and mitigate or eliminate undesired future states by taking some actions proactively. But it is difficult to handle some issues like the data distribution change in streaming data, combination of prediction and decision making, and the huge state space in decision making. In this paper, we propose a proactive decision support method based on deep reinforcement learning and state partition. The predictive analytics part uses deep belief networks with two level incremental training method. The deep reinforcement learning part uses deep belief networks as function approximation which is learned by semi-gradient method. Off-policy is supported through important sampling. Two kinds of state partition and parallel execution methods are proposed to improve the performance. The experimental evaluation in traffic congestion control application shows this method works well in both accuracy and performance. (C) 2017 Elsevier B.V. All rights reserved.
机译：大数据流是一种重要的大数据，我们需要新技术来处理。从在线流数据中获取知识并在线做出决策可以帮助我们从大数据中获得更多价值。主动的决策支持系统可以预测未来状态，并通过主动采取一些措施来缓解或消除不希望的未来状态。但是很难处理一些问题，例如流数据中的数据分布更改，预测和决策制定的结合以及决策制定中巨大的状态空间。本文提出了一种基于深度强化学习和状态划分的主动决策支持方法。预测分析部分使用具有两级增量训练方法的深度置信网络。深度强化学习部分使用深度信念网络作为通过半梯度法学习的函数逼近。通过重要的抽样来支持非政策。提出了两种状态划分和并行执行方法来提高性能。在交通拥堵控制应用中的实验评估表明，该方法在准确性和性能上都很好。 （C）2017 Elsevier B.V.保留所有权利。

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