Experience-Driven Exploration for Efficient API-Free AI Agents

• URL: http://arxiv.org/abs/2510.15259v2
• Date: Sun, 02 Nov 2025 05:44:16 GMT
• Title: Experience-Driven Exploration for Efficient API-Free AI Agents
• Authors: Chenwei Tang, Jingyu Xing, Xinyu Liu, Zizhou Wang, Jiawei Du, Liangli Zhen, Jiancheng Lv,
• Abstract summary: KG-Agent is an experience-driven learning framework that structures an agent's raw pixel-level interactions into a persistent State-Action Knowledge Graph.<n> KG-Agent overcomes inefficient exploration by linking functionally similar but visually distinct GUI states.<n>We demonstrate significant improvements in exploration efficiency and strategic depth over the state-of-the-art methods.
• Score: 34.38668336861503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing software lacks accessible Application Programming Interfaces (APIs), requiring agents to operate solely through pixel-based Graphical User Interfaces (GUIs). In this API-free setting, large language model (LLM)-based agents face severe efficiency bottlenecks: limited to local visual experiences, they make myopic decisions and rely on inefficient trial-and-error, hindering both skill acquisition and long-term planning. To address these challenges, we propose KG-Agent, an experience-driven learning framework that structures an agent's raw pixel-level interactions into a persistent State-Action Knowledge Graph (SA-KG). KG-Agent overcomes inefficient exploration by linking functionally similar but visually distinct GUI states, forming a rich neighborhood of experience that enables the agent to generalize from a diverse set of historical strategies. To support long-horizon reasoning, we design a hybrid intrinsic reward mechanism based on the graph topology, combining a state value reward for exploiting known high-value pathways with a novelty reward that encourages targeted exploration. This approach decouples strategic planning from pure discovery, allowing the agent to effectively value setup actions with delayed gratification. We evaluate KG-Agent in two complex, open-ended GUI-based decision-making environments (Civilization V and Slay the Spire), demonstrating significant improvements in exploration efficiency and strategic depth over the state-of-the-art methods.

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