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A key method for creating Artificial Intelligence (AI) agents isReinforcement Learning (RL). However, constructing a standalone RL policy thatmaps perception to action directly encounters severe problems, chief among thembeing its lack of generality across multiple tasks and the need for a largeamount of training data. The leading cause is that it cannot effectivelyintegrate prior information into the perception-action cycle when devising thepolicy. Large language models (LLMs) emerged as a fundamental way toincorporate cross-domain knowledge into AI agents but lack crucial learning andadaptation toward specific decision problems. This paper presents a generalframework model for integrating and learning structured reasoning into AIagents' policies. Our methodology is motivated by the modularity found in thehuman brain. The framework utilises the construction of intrinsic and extrinsicfunctions to add previous understandings of reasoning structures. It alsoprovides the adaptive ability to learn models inside every module or function,consistent with the modular structure of cognitive processes. We describe theframework in-depth and compare it with other AI pipelines and existingframeworks. The paper explores practical applications, covering experimentsthat show the effectiveness of our method. Our results indicate that AI agentsperform and adapt far better when organised reasoning and prior knowledge areembedded. This opens the door to more resilient and general AI agent systems.

Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning