OpenKAGE: Open-Ended Lifelong Learning in the Open World via Knowledge-Sharing Agent-Group Evolution

Open-ended lifelong learning requires agents to accumulate and reuse knowledge while autonomously selecting goals. Existing research often pursues lifelong learning under predefined tasks, curricula, or objectives, and largely entrusts self-evolution to a single agent. We propose OpenKAGE, a training-free framework that realizes this vision through group-level knowledge sharing in open-world environments: (1) each agent maintains a local knowledge graph with two layers—a private memory layer for situated experience and a shareable experience layer holding transferable knowledge as textual records and executable skills; (2) a semantic aggregator periodically consolidates the experience layers into a shared graph, which agents access through intent-aware subgraph retrieval, while memory-layer records remain local. In 30-game-day Minecraft evaluations with two backbone models, OpenKAGE achieves obsidian acquisition and Nether entry, milestones not reached by a matched planner-only baseline, while reducing mean deaths. A cross-group, cross-backbone transfer experiment further suggests that accumulated knowledge can benefit newly initialized agents. Together, these results demonstrate sustained autonomous learning and effective knowledge reuse within the evaluated horizon, supporting OpenKAGE as a practical framework for open-ended lifelong learning.