Video: Innovation – High Confidence Generalization for Reinforcement Learning

In Reinforcement learning, a decision-support system generates training data by interacting with, or sensing, the world. The system must learn the consequences of its actions through trial and error, rather than being explicitly told the correct action. The ability to generalize confidence in this context is critically essential to applications of reinforcement learning in dynamic military mission contexts, particularly given the pervasive and heterogeneous nature of sensing in the IoBT.