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…