arXiv:2605.29032v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably exploit minor model inaccuracies, leading to simulator exploitation and a reality gap where policies succeed in simulation but fail in the real world. We propose that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player. We provide a comprehensive t
The increasing sophistication and widespread adoption of reinforcement learning models highlight the critical need to address their inherent vulnerabilities to simulator exploitation for real-world deployment.
This research proposes a new paradigm for simulator learning focusing on strategic robustness rather than mere predictive accuracy, which is crucial for developing reliable and deployable AI agents.
The explicit formulation of simulator learning as a zero-sum minimax game against adversarial policies introduces a more robust and game-theoretic approach to model-based reinforcement learning.
- · AI developers
- · Robotics industry
- · Autonomous systems
- · Machine learning researchers
- · Traditional predictive model approaches
- · Systems unprepared for adversarial exploitation
More resilient and trustworthy AI models will emerge, capable of performing reliably outside of controlled simulation environments.
This methodology could accelerate the deployment of autonomous agents into high-stakes real-world applications by mitigating the reality gap.
Increased reliability of AI systems could lead to broader societal integration of AI, potentially transforming industries reliant on complex decision-making in unpredictable environments.
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