Sample-Efficient Reinforcement Learning for Artificial Intelligence Agriculture Pest Management Under Delayed Economic Injury Thresholds: A Computer Regret-Bound Evaluation

Pest management in agriculture is a sequential decision problem in which the economic injury threshold (EIT) is not observed at the moment of intervention but is revealed only after the season, when the cumulative damage and the cost of control have both been realised. Reinforcement learning (RL) is a natural formalism for this problem, yet the sample cost of RL is the binding constraint in agricultural deployment, where each episode is a season and the number of seasons available for learning is small. The problem is formalised as a finite-horizon Markov decision process with a delayed, threshold-triggered reward, and a lower bound on the regret of any uniformly efficient algorithm is derived. A threshold-aware optimistic algorithm is then proposed, and its regret is shown to scale with the square root of the number of episodes and with the delay horizon, rather than with the number of state-action pairs. The analysis is evaluated computationally: a before-after-control-impact design with an intraclass correlation of 0.64, a between-cluster standard deviation of 6.0 and a residual standard deviation of 4.5 was used to characterise the operating characteristics of the evaluation, with 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design requires 54 clusters to detect a 5-percentage-point effect at 80% power. The contribution is a regret bound that separates the cost of delay from the cost of exploration, and a demonstration that sample-efficient RL for EIT management is feasible at the season counts that agricultural deployment actually permits.