Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussian-softmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussian-softmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.
翻译:基于系统条件,从而有可能延长剩余有用寿命期,根据系统条件确定最佳操作,这在积极管理复杂系统的可用性、维护性和成本方面具有巨大潜力。强化学习(RL)算法因其学习能力而特别适合这类问题。规范操作的一个特殊情况是权力分配任务,可被视为一个顺序分配问题,在这种分配空间受简单x限制的束缚的情况下,可将其视为一个行动空间的顺序分配问题。这种顺序分配问题的一般连续行动空间解决方案仍然是RL算法的一个开放式研究问题。在连续行动空间,在强化学习中应用的标准高斯政策不支持简单x限制,而高斯软化政策则在培训中带有偏见。在这项工作中,我们提议Dirichlet政策是连续分配任务和分析其政策梯度的偏差和差异。我们证明,Dirichlet政策是没有偏差的,并且为高斯软化政策提供更快的趋同、更好的性能和超度强度强度强度的研究。此外,在连续行动空间中,在强化学习中应用标准政策政策政策不支持简单x的限制,而高斯-软化政策在培训中引入偏差式算法政策政策政策政策政策,我们提议Dirchletlet政策分配法,我们提议Dirtal政策应用Dirtal-s-listral-s-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-res-violvicutututututututututututututututi-viction-viction-vici-vicis-vicisi-vicisi-vicism-vicism-vical-vical-vical-i-vical-vical-vical-vical-vical-vical-vical-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I