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Historical failure records can be transformed into diverse testing scenarios for autonomous driving, revealing critical system vulnerabilities with minimal effort.
Decomposing Bellman values into a graph of simpler objectives lets agents master complex, high-dimensional tasks with less tuning and better safety.
Forget hand-engineering initial conditions for robust RL: this method *learns* which conditions are feasible while simultaneously training a safe policy.
