Facilitating Zero Shot Decision Generalization through Conservative Offline Reinforcement Learning and Semantic Policy Pre training with Large Language Models
DOI:
https://doi.org/10.66280/ijair.v1i2.151Keywords:
Zero-Shot Generalization, Offline Reinforcement Learning, Large Language Models, Socio-Technical Systems, Semantic Policy Pre-training, Infrastructure ResilienceAbstract
The advancement of autonomous systems requires a paradigm shift from narrow task optimization toward robust zero-shot decision generalization across heterogeneous environments. This paper investigates the integration of conservative offline reinforcement learning with semantic policy pre-training facilitated by large language models to address the limitations of traditional behavioral cloning and online exploration. Traditional reinforcement learning often fails when encountering out-of-distribution states, leading to catastrophic performance degradation in high-stakes socio-technical infrastructures. By leveraging the vast world knowledge encoded in large language models, we propose a framework that maps high-level semantic intents to low-level control policies, effectively creating a common grounding for diverse decision-making tasks. Conservative offline reinforcement learning serves as the stabilizing mechanism, ensuring that the learned policies remain within the support of the training data while mitigating overestimation bias in value functions. This interdisciplinary approach emphasizes the structural trade-offs between exploration and safety, focusing on the deployment of resilient AI in critical sectors such as energy management, autonomous logistics, and large-scale urban infrastructure. We provide an extensive analysis of system architecture, the governance of semantic priors, and the long-term sustainability of pre-trained models in evolving operational contexts. Our findings suggest that the synergy between linguistic reasoning and conservative value estimation provides a robust pathway for achieving generalization without the need for extensive real-world environmental interaction.
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