Medium

This post details the application of reinforcement learning to budget-constrained recommendations, specifically focusing on optimizing the trade-off between recommendation quality and the cost of serving those recommendations. It introduces a novel approach using a deep Q-network (DQN) to learn a policy that balances these competing objectives, aiming to maximize member engagement while staying within a defined budget. The system addresses the challenge of sparse rewards and the need for efficient exploration in a large recommendation space.