arXiv:2601.19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.g. simulated) environments. In this work, we tackle this challenge by utilizing suboptimal yet conservative policies (e.g., obtained from offline data or simulators) as priors. Our approach, SOOPER, uses probabilistic dynamics models to optimistically explore, yet pessimistically fall back to the conservative policy prior if needed. We prove that SOOPER guarantees safety throughout learning, and establish converge
The increasing push for real-world deployment of AI agents and RL models necessitates robust safety mechanisms to move beyond simulated environments.
Safe exploration is a critical bottleneck for deploying autonomous reinforcement learning agents in sensitive or physical environments, directly impacting commercial viability and public trust.
The ability to formally guarantee safety during online learning opens up industrial and high-stakes applications for AI that were previously limited due to unpredictability.
- · AI Agent developers
- · Robotics industry
- · Industrial automation
- · Logistics and autonomous vehicles
- · Traditional control systems (in certain applications)
- · Companies unable to integrate advanced safety protocols
Increased real-world deployment and commercialization of advanced reinforcement learning-based autonomous systems.
Accelerated development of AI agents in physical domains, reducing the need for extensive human supervision in dangerous or repetitive tasks.
Enhanced societal acceptance and regulatory frameworks for autonomous AI, leading to broader integration into critical infrastructure.
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Read at arXiv cs.AI