Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts
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This December 2025 research introduces **Contextual Sample Efficiency (CSE)**, a novel algorithm designed to improve **zero-shot generalization** in reinforcement learning using minimal training data. Standard methods often require expensive, diverse simulations to prepare agents for new environments, but this approach leverages **linear approximations** of environmental dynamics to bypass extensive sampling. By incorporating **gradient information** regarding how rewards and transitions change with different parameters, the authors enable agents to adapt to unseen scenarios without direct experience. Their mathematical framework, based on **Contextual Bellman Equations**, provides a formal proof that these linear estimates can effectively bound performance errors in varying contexts. Testing across diverse **MuJoCo and robotics simulations** demonstrates that CSE consistently outperforms traditional baselines and matches complex existing methods. Ultimately, the study offers a scalable, **computationally efficient strategy** for deploying robust agents in real-world systems where environmental conditions are unpredictable.
Source:
December 2025
Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts
University of California, Los Angeles
James Chapman, Kedar Karhadkar, Guido Montúfar
https://arxiv.org/pdf/2507.07348
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