Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

TL;DR

This paper introduces an active feedback acquisition model for autonomous agents that interact with environments to discover and verify feedback without predefined measurements, enhancing adaptability in dynamic settings.

Contribution

It presents a novel method enabling agents to proactively obtain environmental feedback through action-induced changes, eliminating reliance on predefined reward signals.

Findings

Improves feedback acquisition efficiency

Enhances robustness in dynamic environments

Reduces dependence on predefined measurements

Abstract

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.