This study considers a delivery platform that offers shoppers compensation for taking online orders to nearby customers. A Markov decision model represents uncertain order arrivals, shopper availability and offer acceptance. Neural approximate dynamic programming assigns deliveries, while a deep double Q-network adapts prices. Experiments show that coordinating assignment and compensation improves delivery-cost efficiency compared with fixed-price and myopic alternatives; flexible delivery windows and multi-destination routes offer further gains.