Replicating Expert Behavior via Imitation Learning

Mon, 01 Jan 0001 00:00:00 +0000

Problem

Oil & Gas production assets are run through SCADA systems, where experienced operators read many sensor streams and decide how to act. That know-how is hard to write down as rules. This project set out to learn those decisions directly from recorded expert behavior, so that they can be made autonomously.

Approach

The system is built on Generative Adversarial Imitation Learning (GAIL). Instead of hand-designing a reward, two networks are trained against each other on multi-sensor SCADA data: a discriminator learns to tell the experts' recorded decisions apart from the policy’s, and the policy learns to make decisions the discriminator cannot tell apart from the experts’. The trained policy then reproduces expert behavior.

Industrial-Automation on Omanshu Thapliyal

Replicating Expert Behavior via Imitation Learning

Problem

Approach