MO-DQN Sequential MORL Pivot Architecture replaces gradient-based trade-off mechanisms with policy-level sequential optimization for multi-objective food recommendation. Data flows from a user-food bipartite graph through health tag enrichment and frozen GNN representation learning, then into a sequential MDP environment where a conditional policy balances competing objectives via Dirichlet-sampled weight vectors. This approach enables horizon-aware trade-off allocation across recommendation quality and nutritional constraints, validated through rollouts over a weight grid to select the optimal operating point. Fork this diagram to customize the MDP state representation, modify the candidate pool scoring, or integrate alternative weight sampling strategies for your multi-objective recommendation pipeline.