PhD Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning

NSH 4305

Abstract:  Reinforcement Learning (RL) presents great promises for autonomous agents. However, when using robots in a safety critical domain, a system has to be robust enough to be deployed in real life. For example, the robot should be able to perform across different scenarios it will encounter. The robot should avoid entering undesirable and irreversible [...]