Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)

Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-real gap. This gap can reduce the effectiveness of the developed controller. In th... View More