Adversarial robustness in RL is difficult because perturbations affect entire trajectories: strong attacks can break learning, while weak attacks yield little robustness, and the appropriate strength varies by state. We propose $α$-reward-preserving attacks, which adapt the strength of the adversary so that an $α$ fraction of the nominal-to-worst-case return gap remains achievable at each state. In deep RL, we use a gradient-based attack direction and learn a state-dependent magnitude $η\le η_{\mathcal B}$ selected via a critic $Q^π_α((s,a),η)$ trained off-policy over diverse radii. This adaptive tuning calibrates attack strength and, with intermediate $α$, improves robustness across radii while preserving nominal performance, outperforming fixed- and random-radius baselines.

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