AI Research
Auditability Is Not One Property: Rule Overlap, Behavioural Agreement, and Composition in Reinforcement Learning
Deconstructing black-box policies into modular, verifiable units for improved reinforcement learning accountability.
Reinforcement-learning (RL) policies are often distributed as opaque neural checkpoints, while training logs show that a run occurred without explaining what the policy learned. We study whether independently trained policies can be represented and composed through auditable disc...
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ArXiv CS.LG
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ArXiv CS.LG
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This research breaks down the monolithic nature of reinforcement learning black boxes into modular, verifiable components. By enabling cross-policy auditability, it provides a foundation for safer and more transparent deployment of complex autonomous systems.
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