Abstraction of the event-triggered learning framework. Structured decisions about when to learn are obtained based on a comparison of a model-based reference signal and incoming data from the system.
The ability to learn is an essential aspect of future intelligent systems that are facing uncertain environments. However, the process of learning a new model or behavior often does not come for free, but involves a certain cost. For example, gathering informative data can be challenging due to physical limitations, or updating models can require substantial computation. Moreover, learning for autonomous agents often requires exploring new behavior and thus typically means deviating from nominal or desired behavior. Hence, the question of when to learn is essential for the efficient and intelligent operation of autonomous systems.
Event-triggered learning (ETL) was proposed in our publication [ ] for making principled decisions on when to learn new dynamics models and applied for efficient communication in distributed systems. Information exchange in distributed systems is a key aspect in solving collaborative tasks. Communication often takes place over wireless networks, and therefore, needs to be used carefully to avoid overloading the network. Dynamical models are deployed to predict other agents' behavior and therefore, accurate models are essential to reduce communication effectively. We developed a stochastic trigger to decide when to learn a new model and derive statistical guarantees that the triggering happens at the right time. In a collaboration with TU Berlin, we validated the proposed method experimentally on IMU sensor networks [ ].
While effectively reducing communication in distributed systems, the developed ideas are more general and address in their core the question when to learn with possible extensions in different directions. Among others, in [ ], we investigate their generalization to cost signals. Using different performance signals to perform decisions in reinfocement learning is an important aspect of ongoing research in which we link the exploration-exploitation trade-off to ETL.