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This paper proposes a strategic robustness objective for learning simulators in model-based reinforcement learning, formulated as a minimax game between a model player and an adversarial policy player. Theoretical guarantees and a provably convergent algorithm are provided, with experiments showing reduced prediction error and improved real-world policy transfer.
This paper studies distillation attacks where model outputs can enable imitation, proposing a minimax game framework and a forward-pass-only defense called Product-of-Experts, showing that adaptive students recover more capability than passive evaluation suggests.