What is off-policy vs. on-policy testing?
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On-Policy Testing
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In on-policy testing, the agent’s performance is evaluated using the same policy that is currently being learned or deployed.
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The actions used for testing are exactly those chosen by the policy itself.
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This approach measures how well the agent performs when it follows its own decision-making strategy in real time.
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Example: In reinforcement learning, testing a robot by letting it move using its current trained policy and recording its average reward.
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Pros: Reflects the true performance of the policy being tested.
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Cons: Can be costly or risky if the policy is not yet stable, especially in real-world settings.
Off-Policy Testing
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In off-policy testing, the agent’s performance is evaluated using data collected from a different policy (often called a behavior policy).
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Instead of running the agent live, it tests how the target policy would have performed on previously collected experiences.
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Example: Using logs of user interactions from a website (collected under an older policy) to test how a new recommendation policy would perform.
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Pros: Safer and cheaper because it avoids live testing; useful when real-world trials are risky.
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Cons: Can be biased or inaccurate if the collected data does not cover enough of the action space of the target policy.
Key Difference
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On-policy testing = evaluate while following the current policy directly.
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Off-policy testing = evaluate using past data generated by another policy.
👉 In short, on-policy testing shows actual performance in real-time, while off-policy testing estimates potential performance using old data.
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