contextspolicies

Context policies are decision rules that choose actions based on contextual information about the current situation. The concept appears in reinforcement learning, contextual bandits, and other context-aware decision systems.

A contextual policy is often written as π(a|s,c), where s is the state or observation and c is the context. Context can be observable features such as user demographics, time of day, sensor readings, or past interactions, or latent variables inferred from data. By conditioning actions on context, the policy can adapt its behavior to different situations rather than applying a single fixed rule.

Implementations typically use parameterized function approximators that ingest context along with state. Context can be used to select among actions, to modulate the policy, or to switch between sub-policies. This makes contextual policies a natural fit for problems where the environment or user needs vary over time or across individuals.

Key design considerations include how to represent and collect context, how to handle non-stationarity and distribution shifts across contexts, and how to balance performance with safety and interpretability when contexts are high-dimensional or sparse. Robustness to context sparsity and the risk of overfitting to specific contexts are common challenges.

Applications span personalized recommendations, adaptive robotics, healthcare decision support, and smart infrastructure that adapts to changing conditions. Evaluation typically uses context-dependent metrics such as contextual regret, context-conditioned reward, and cross-context generalization. See also contextual bandits, reinforcement learning, policy learning, and context-aware systems.