Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
A key method for creating Artificial Intelligence (AI) agents is
Reinforcement Learning (RL). However, constructing a standalone RL policy that
maps perception to action directly encounters severe problems, chief among them
being its lack of generality across multiple tasks and the need for a large
amount of training data. The leading cause is that it cannot effectively
integrate prior information into the perception-action cycle when devising the
policy. Large language models (LLMs) emerged as a fundamental way to
incorporate cross-domain knowledge into AI agents but lack crucial learning and
adaptation toward specific decision problems. This paper presents a general
framework model for integrating and learning structured reasoning into AI
agents' policies. Our methodology is motivated by the modularity found in the
human brain. The framework utilises the construction of intrinsic and extrinsic
functions to add previous understandings of reasoning structures. It also
provides the adaptive ability to learn models inside every module or function,
consistent with the modular structure of cognitive processes. We describe the
framework in-depth and compare it with other AI pipelines and existing
frameworks. The paper explores practical applications, covering experiments
that show the effectiveness of our method. Our results indicate that AI agents
perform and adapt far better when organised reasoning and prior knowledge are
embedded. This opens the door to more resilient and general AI agent systems.
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Filippos Christianos
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Georgios Papoudakis
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Matthieu Zimmer
658a4c122021ba68d7d154d9
Thomas Coste
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Thomas
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Zhihao Wu
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Jingxuan Chen
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Khyati Khandelwal
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Khyati Khandelwal
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James Doran
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Xidong Feng
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Jiacheng Liu
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Zheng Xiong
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Yicheng Luo
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Jianye Hao
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Kun Shao
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Haitham Bou-Ammar
claimed_verified
2024-01-10T08:39:03.833Z
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Haitham Bou Ammar
user
hba123
658a4c122021ba68d7d154e5
Jun Wang
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
A key method for creating Artificial Intelligence (AI) agents is
Reinforcement Learning (RL). However, constructing a standalone RL policy that
maps perception to action directly encounters severe problems, chief among them
being its lack of generality across multiple tasks and the need for a large
amount of training data. The leading cause is that it cannot effectively
integrate prior information into the perception-action cycle when devising the
policy. Large language models (LLMs) emerged as a fundamental way to
incorporate cross-domain knowledge into AI agents but lack crucial learning and
adaptation toward specific decision problems. This paper presents a general
framework model for integrating and learning structured reasoning into AI
agents' policies. Our methodology is motivated by the modularity found in the
human brain. The framework utilises the construction of intrinsic and extrinsic
functions to add previous understandings of reasoning structures. It also
provides the adaptive ability to learn models inside every module or function,
consistent with the modular structure of cognitive processes. We describe the
framework in-depth and compare it with other AI pipelines and existing
frameworks. The paper explores practical applications, covering experiments
that show the effectiveness of our method. Our results indicate that AI agents
perform and adapt far better when organised reasoning and prior knowledge are
embedded. This opens the door to more resilient and general AI agent systems.
A framework integrates structured reasoning into AI agents' policies using intrinsic and extrinsic functions, enhancing adaptability and performance over diverse tasks by combining prior knowledge with modular learning.
Reinforcement Learning (RL) Large language models (LLMs) intrinsic functions extrinsic functions structured reasoning cognitive processes modular learning