Accelerating Rapid Task Adaptation via Meta Reinforcement Learning and Large Language Model Prompt Optimization for Dynamic Decision Environments
DOI:
https://doi.org/10.66280/cis.v1i1.151Abstract
The increasing complexity of modern industrial and socio-technical systems requires autonomous agents capable of transitioning between disparate tasks with minimal latency and high reliability. Traditionally, reinforcement learning frameworks have struggled with out-of-distribution shifts in dynamic environments, often requiring extensive retraining or fine-tuning when faced with novel task constraints. This research paper explores a hybrid architectural approach that integrates Meta Reinforcement Learning with Large Language Model prompt optimization to bridge the gap between low-level control and high-level strategic reasoning. By utilizing Meta Reinforcement Learning for rapid parameter adaptation and Large Language Models for context-aware objective alignment, the proposed system-level framework facilitates a dual-track cognitive architecture. We examine the structural trade-offs inherent in this integration, specifically focusing on the computational overhead of real-time prompt engineering versus the sample efficiency gains in environmental interaction. The discussion emphasizes the infrastructure requirements for deploying such hybrid models in large-scale systems, the governance challenges regarding model transparency and fairness, and the long-term sustainability of maintaining high-dimensional decision-making agents in fluctuating markets or physical environments. This paper argues that the synergy between non-symbolic learning and symbolic prompt refinement provides a robust pathway toward achieving resilient, general-purpose artificial intelligence in critical infrastructure and complex decision-making domains.
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