SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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RSD-Poker is a certification framework for residual policies in imperfect‑information games that adapts to game structure and remains robust to shifts in deployment state mixtures. It freezes a bank of residual families and a policy‑visible partition, then assigns each candidate‑group pair weighted upper and lower certificates for risk and utility. The framework guarantees that, with high probability, the selected candidate map meets its declared risk budget and utility bounds across a predeclared uncertainty set of deployment group proportions.
The paper investigates the problem of sharing a single critic across multiple parallel environments in reinforcement learning. It shows that when environments assign different expected returns to the same state, a shared critic must reconcile conflicting value targets, which can distort advantage estimates and misguide policy updates. The authors propose a simple fix—providing the critic with the environment index—demonstrating through bandit models and experiments on CartPole, MuJoCo, BipedalWalker, and 16 Procgen games that this conditional critic stabilizes learning and boosts returns, achieving a 40.8% improvement in aggregate normalized return on unseen levels.
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