New RL paper: regret minimization when observing rewards costs money per action
burkov · x · 2026-10-16
- Setting: A sequential decision problem where an agent repeatedly picks actions but must pay a (action-dependent) cost to observe the reward of the chosen action.
- Why it matters: Mirrors online advertising (testing premium audiences is expensive) and generative AI evaluation (expert feedback on complex outputs is costly), turning exploration-exploitation into a three-way tradeoff: explore, earn rewards, and decide whether information is worth its price.
- Contribution: Characterizes and minimizes regret when reward observations are optional, action-dependent in cost, and potentially correlated across similar actions; designs algorithms that decide both which action to play and when to pay for feedback, plus measures of problem difficulty.
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