Contextual Intelligence The Next Leap for Reinforcement Learning

• URL: http://arxiv.org/abs/2604.02348v1
• Date: Tue, 17 Feb 2026 11:12:05 GMT
• Title: Contextual Intelligence The Next Leap for Reinforcement Learning
• Abstract summary: Reinforcement learning (RL) has produced spectacular results in games, robotics, and continuous control.<n>Recent work on contextual RL (cRL) shows that exposing agents to environment characteristics -- contexts -- can improve zero-shot transfer.<n>We propose a novel taxonomy of contexts that separates allogenic (environment-imposed) from autogenic (agent-driven) factors.
• Score: 4.1558950738080105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 learning mechanisms; (3) Integration of abstract, high-level contexts to incorporate roles, resource & regulatory regimes, uncertainties, and other non-physical descriptors that crucially influence behavior. We envision context as a first-class modeling primitive, empowering agents to reason about who they are, what the world permits, and how both evolve over time. By doing so, we aim to catalyze a new generation of context-aware agents that can be deployed safely and efficiently in the real world.

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