A policy maps state to action, and reinforcement learning adjusts a policy from reward-bearing experience. Safety constraints and off-policy evaluation matter before learned choices control real tools.

Agent Graphs as Policies Over States

A contextual bandit chooses among routes, observes immediate reward, and balances exploration with exploitation. Safe deployment constrains actions and accounts for selection bias.

Contextual Bandits Learn Routing Under Guardrails

In long graphs, a final outcome reflects many prior decisions. Credit assignment estimates which actions contributed, while accounting for delayed effects and confounding.

Credit Assignment Links Outcomes to Earlier Choices

An option is a temporally extended skill with initiation conditions, an internal policy, and termination. Hierarchical control can reuse subgraphs while preserving when they are valid and how they stop.

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