arXiv:2602.04737v3 Announce Type: replace Abstract: This paper proposes a suite of rationality measures and associated theory for reinforcement learning agents, a property increasingly critical yet rarely explored. We define an action in deployment to be perfectly rational if it maximises the hidden true value function in the steepest direction. The expected value discrepancy of a policy's actions against their rational counterparts, culminating over the trajectory in deployment, is defined to be expected rational risk; an empirical average version in training is also defined. Their difference
The rapid advancement and deployment of AI agents necessitate robust theoretical frameworks for understanding and ensuring their performance and safety, moving beyond experimental observation.
Measuring and theorizing rationality for reinforcement learning agents is crucial for developing reliable, autonomous AI systems that can operate effectively and safely in complex, real-world environments.
This research provides a foundational framework for evaluating and designing more predictable and controllable autonomous AI, shifting agent development towards more rigorous, theoretically-grounded approaches.
- · AI research institutions
- · Developers of autonomous AI agents
- · Industries deploying AI for critical applications
- · Reinforcement learning practitioners
- · AI systems lacking interpretability and robust theoretical grounding
- · Organizations deploying black-box AI without verification
The adoption of these rationality measures will lead to more robust and verifiable autonomous AI systems.
Improved rationality metrics will accelerate the development and trust in AI agents for high-stakes applications, potentially blurring the human-AI decision-making boundary.
A deeper theoretical understanding of AI rationality could inform the design of future 'artificial general intelligence' with quantifiable performance and safety guarantees.
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Read at arXiv cs.LG