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Modern reinforcement learning systems produce many high-quality policiesthroughout the learning process. However, to choose which policy to actuallydeploy in the real world, they must be tested under an intractable number ofenvironmental conditions. We introduce RPOSST, an algorithm to select a smallset of test cases from a larger pool based on a relatively small number ofsample evaluations. RPOSST treats the test case selection problem as atwo-player game and optimizes a solution with provable $k$-of-$N$ robustness,bounding the error relative to a test that used all the test cases in the pool.Empirical results demonstrate that RPOSST finds a small set of test cases thatidentify high quality policies in a toy one-shot game, poker datasets, and ahigh-fidelity racing simulator.

Composing Efficient, Robust Tests for Policy Selection