Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning

In three linesarXiv paper proposing strategic robustness for simulator learning in MBRL. Formulates objective as minimax game between model and adversarial policy player. Proves convergence with sublinear regret bounds and Error-MDP duality. Experiments show 1.5–2.2× reduction in prediction error and simulation-trained policies matching near-optimal real-world performance.

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