simulator-learning

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.