REACT evolves initial states under a joint diversity-and-certainty fitness to produce demonstrations that score higher on a fidelity proxy in gridworlds and early continuous control, but fidelity-based optimization wins for mature policies.
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Surrogate Fitness Metrics for Interpretable Reinforcement Learning
REACT evolves initial states under a joint diversity-and-certainty fitness to produce demonstrations that score higher on a fidelity proxy in gridworlds and early continuous control, but fidelity-based optimization wins for mature policies.