[2604.02348] Contextual Intelligence The Next Leap for Reinforcement Learning

Computer Science > Machine Learning arXiv:2604.02348 (cs) [Submitted on 17 Feb 2026] Title:Contextual Intelligence The Next Leap for Reinforcement Learning Authors:André Biedenkapp View a PDF of the paper titled Contextual Intelligence The Next Leap for Reinforcement Learning, by Andr\'e Biedenkapp View PDF HTML (experimental) Abstract:Reinforcement learning (RL) has produced spectacular results in games, robotics, and continuous control. Yet, despite these successes, learned policies often fail to generalize beyond their training distribution, limiting real-world impact. Recent work on contextual RL (cRL) shows that exposing agents to environment characteristics -- contexts -- can improve zero-shot transfer. So far, the community has treated context as a monolithic, static observable, an approach that constrains the generalization capabilities of RL agents. To achieve contextual intelligence we first propose a novel taxonomy of contexts that separates allogenic (environment-imposed) from autogenic (agent-driven) factors. We identify three fundamental research directions that must be addressed to promote truly contextual intelligence: (1) Learning with heterogeneous contexts to explicitly exploit the taxonomy levels so agents can reason about their influence on the world and vice versa; (2) Multi-time-scale modeling to recognize that allogenic variables evolve slowly or remain static, whereas autogenic variables may change within an episode, potentially requiring different...