All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning
arXiv:2606.01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics. However, inaccuracies of the learned dynamics model are typically exploited by the agent, substantially hampering the capabilities of MBRL methods. We present a framework for dealing with inaccuracies of probabilistic models through targeted handling of uncertainty that effectively mitigates model exploitation. We present recent succe
The increasing complexity and deployment of AI in real-world systems, especially in areas like robotics, necessitates robust methods for managing model uncertainty to ensure safety and reliability.
This research addresses a critical limitation in model-based reinforcement learning (MBRL) by providing a framework to mitigate model exploitation, which is vital for developing trustworthy and performant AI systems.
The proposed framework allows for more reliable and data-efficient learning in MBRL by explicitly handling model inaccuracies, paving the way for wider and safer deployment of AI in sensitive applications.
- · AI/ML researchers
- · Robotics industry
- · AI safety and ethics organizations
- · Autonomous systems developers
- · Developers relying solely on black-box, uninterpretable models
- · Sectors unprepared for robust AI safety standards
Improved performance and safety of model-based reinforcement learning agents in real-world scenarios.
Accelerated adoption of AI in high-stakes environments such as autonomous driving and critical infrastructure management due to enhanced reliability.
Potential for new regulatory frameworks and industry standards centered around quantifiable model uncertainty and interpretability in AI deployments.
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