Like humans, machines can learn by observing and repeating behaviors. However, in its most basic form, this type of imitation learning can lack the ability to translate learning to real-world scenarios. To address this challenge, Emerging Technology Center researchers have been looking to Inverse Reinforcement Learning (IRL) techniques—an area of machine learning—to more efficiently and effectively teach novices how to perform expert tasks, achieve robotic control, and perform activity-based intelligence.
Data is key to the mission of the U.S. Department of Defense (DoD) and the intelligence community (IC), allowing the DoD to make informed decisions based on accurate understandings. With technology, data can now be collected from many sources and put together to see a complete picture. The challenge is in sifting through this vast amount of data to find the signals buried in the white noise of routine observations. If data could be analyzed faster and more precisely, decisions based on complex data could be made more accurately and in real-time.
In this demo, you are able to interact with an MCEIRL model that was trained on Automatic Identification System (AIS) nautical vessel data provided by the U.S. Coast Guard for ships that entered New York Harbor in August of 2016. The goal of this demonstration is to illustrate how MCEIRL models represent the sequential behavior of ships as they navigate to their destinations.