让智能体主动发现环境反馈，无需预设奖励信号。
Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements
- 通过动作引发的环境变化自动识别反馈信号。
- 在无预设测量条件下仍能高效定位关键因素。
- 适合开放动态环境中自主决策的智能系统。
获取可靠的环境反馈是智能体评估自身行为正确性并积累可复用知识的基础能力。然而，现有方法多依赖预定义度量或固定奖励信号，限制了其在开放动态环境中对新动作的适应性，因新动作可能需要此前未知的反馈形式。为此，本文提出一种主动获取反馈模型（Actively Feedback Getting），使智能体能主动与环境交互，自主发现、筛选和验证反馈，而无需依赖预设测量。该方法不假设显式反馈定义，而是利用动作引起的环境差异，基于动作必然导致可测量变化这一观察，识别事先未指定的目标反馈。此外，引入由内部目标（如提升精度、准确性和效率）驱动的自触发机制，实现无需外部指令的自主动作规划与调整，从而更快、更聚焦地获取反馈。实验结果表明，所提主动方法显著提升了因素识别的效率与鲁棒性。
原文摘要 · Abstract (English)
Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applicability in open-ended and dynamic environments where new actions may require previously unknown forms of feedback. To address these limitations, this paper proposes an Actively Feedback Getting model, in which an AI agent proactively interacts with the environment to discover, screen, and verify feedback without relying on predefined measurements. Rather than assuming explicit feedback definitions, the proposed method exploits action-induced environmental differences to identify target feedback that is not specified in advance, based on the observation that actions inevitably produce measurable changes in the environment. In addition, a self-triggering mechanism, driven by internal objectives such as improved accuracy, precision, and efficiency, is introduced to autonomously plan and adjust actions, thereby enabling faster and more focused feedback acquisition without external commands. Experimental results demonstrate that the proposed active approach significantly improves the efficiency and robustness of factor identification.
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