arXiv:2408.08812v2 Announce Type: replace Abstract: Deployed reinforcement learning agents often face safety requirements that are specified only after training, such as new hazard maps, revised risk thresholds, or behavioral alignment constraints. We study zero-update deployment-time adaptation, where a fixed library of risk-neutral source policies is reused under a newly specified reward-risk tradeoff. We propose TRAM (Test-Time Risk Adaptation via Mixture of Agents), a source-scored composition rule that evaluates each source policy under the target reward and an occupancy-based deployment
The increasing complexity and deployment of AI agents in real-world scenarios necessitate robust safety and adaptation mechanisms, especially when initial training conditions do not perfectly align with deployment realities.
Organizations deploying AI agents require methods to adapt them to new safety constraints and risk profiles post-training, directly impacting the reliability, trustworthiness, and widespread adoption of autonomous systems.
The ability to dynamically adapt AI agents to new risk parameters at deployment time without retraining significantly enhances their flexibility and safety for real-world applications, accelerating deployment in sensitive areas.
- · AI deployment platforms
- · Robotics
- · Autonomous systems developers
- · High-stakes industries (e.g., defense, medicine, logistics)
- · Legacy AI safety methodologies
- · AI systems lacking adaptive safety features
AI agents become more adaptable and safer when deployed in dynamic and unforeseen risk environments, reducing the cost and time associated with retraining.
Increased trust and faster adoption of AI in sectors with high safety compliance due to the ability to specify and adapt risk parameters post-training.
This could lead to a 'risk-as-a-service' paradigm for AI deployments, where specialized systems manage and adapt agent risk profiles dynamically.
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