A contextual bandit chooses among routes, observes immediate reward, and balances exploration with exploitation. Safe deployment constrains actions and accounts for selection bias.

Contextual Bandits Learn Routing Under Guardrails

Agents may learn compressed messages that improve reward but hide unsafe coordination or exploit simulator quirks. Evaluate semantics, robustness, and controllability rather than performance alone.

Emergent Agent Communication Requires Interpretability Tests

In this situation—learning which explanation style helps a learner answer the next question—which choice best applies “Contextual Bandits Learn Routing Under Guardrails”?

Off-policy evaluation uses logged actions, outcomes, and selection probabilities to estimate a policy not yet deployed. Poor coverage makes estimates unstable or impossible.

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