Abstract
Automating Printer Diagnostics to solve customer issues presents lot of challenges. If we can solve this
problem in a reliable way, it can help to save costs which are incur for every phone call made to call
agents by customers. Instead of customer calling a support agent, a tool should guide customers (either
residing at customer’s computer or over cloud) how to fix those issues. There are rule-based and Machine
learning based solutions developed to solve this issue using call agent record /Telemetry, but to the
best of our knowledge this problem has not been solved reliably. We present a machine learning
based way to solve this problem using model free reinforcement learning algorithms. Reinforcement
Learning Paradigm is a natural fit to this problem as it solves the problem of Sequential decision Making,
which is what a call agent does when he interacts with the customer over call. A Reinforcement
Learning (RL) agent learns from the environment while interacting with it and suggest an optimal action
based upon current state of the environment. The main idea proposed here is how to specify Printer
Diagnostics problem in Reinforcement Learning Framework. To that we propose 1) a way to design
the state/action/reward spaces and 2) a way to generate simulated environment (set of episodes) using
Printer hardware simulator. Using these two ideas we can use standard model free reinforcement learning
algorithms like MonteCarlo/TemporalDifference/QLearning to learn value function and update optimal
policy. The salient feature of this approach, as applied to Printer Diagnostics, is that a fix is predicted
by sensing the customer environment holistically unlike other existing approaches to Printer Diagnostics
where decisions are made based upon certain aspects of the user environment.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
Recommended Citation
INC, HP, "A REINFORCEMENT LEARNING APPROACH TO PRINTER DIAGNOSTICS", Technical Disclosure Commons, (April 22, 2021)
https://www.tdcommons.org/dpubs_series/4244