The Classification of Workforce Requirement Planning for Service Oriented Operations

In today’s world of competitive international economy sectors, service industry orservice sector oriented businesses, the key point is to maximize the efficiency and sustainability of the business directly related with optimal planning of the workload and distributing them among the employees. Helpdesks and operation centers are one of the fastest developing service area of this sector. This paper compares the machine learning algorithms that can be used for the classification of workforce requirements for a bank operation center which provides support to reduce operational workload of bank branches. Classification of the workload based on the quantity of Money Order and EFT operations within time zones aids in the management of workforce teams and distribution of jobs between team members

Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past