Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm

Understand

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios.

• However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures.
• Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system.
• AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems.