Autonomous Learning Through Self-Driven Exploration and Knowledge Structuring for Open-World Intelligent Agents

This study focuses on the problem of autonomous learning for intelligent agents in open-world environments and proposes an agent algorithm framework oriented toward self-exploration and knowledge accumulation. The framework couples hierarchical perception modeling, dynamic memory structures, and knowledge evolution mechanisms to achieve an adaptive closed loop from environmental perception to decision optimization. First, a perception encoding and state representation module is designed to extract multi-source environmental features and form dynamic semantic representations. Then, an intrinsic motivation generation mechanism is introduced, enabling the agent to maintain continuous exploration even without external rewards, thus promoting active discovery and accumulation of knowledge. Meanwhile, a jointly optimized policy network and knowledge updating module is constructed, allowing the agent to continuously integrate new experiences and refine old knowledge during long-term interactions, forming a stable and scalable knowledge structure. Experimental results show that the model achieves superior performance in uncertainty suppression, policy consistency maintenance, and behavioral deviation control, demonstrating its effectiveness and robustness in open-world tasks. This research enriches the theoretical foundation of autonomous learning and provides a feasible technical pathway for building general intelligent systems with self-driven and continuously evolving capabilities.