Must Decision Making Be Trajectory-Centered? Behavioral Structure as a Relation-Centered Alternative

Reinforcement learning is commonly learned from trajectories: finite sequences generated by interactions between an agent and its environment. Yet trajectories are samples of realized behavior, not necessarily the underlying structure of what behaviors are possible. We ask whether decision making must be organized around observed trajectories, or can instead be organized around the behavioral relations revealed by them. We introduce Behavioral Structure, a relation-centered framework that represents action-conditioned relations among states and composes them directly for decision making. On sparse long-horizon goal-directed control, Behavioral Structure reaches 32% success versus 17% for goal-conditioned Decision Transformer and 13% for Decision Transformer on Ant. When training trajectories are deliberately fragmented across withheld behavioral boundaries, Behavioral Structure achieves 32% versus 2% for endpoint-conditioned Decision Diffuser, while retaining 36% success at composition depths 4–5 versus 0%. These results support a view in which trajectories describe realized experience while behavioral relations provide an alternative substrate for organizing decisions.