CIG-RL: Curiosity-Driven Information-Guided Reinforcement Learning for Source Term Estimation in Uncertain Environments

2026-08-31

AI Summary

为解决在不确定环境中源项估计问题，提出了一种基于好奇心驱动的信息引导强化学习方法，通过主动探索未充分探索的状态转换和引入适应性感知奖励来提高策略的鲁棒性和效率。

Abstract

Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identifying hazardous gas releases. Information-theoretic approaches have been adopted for autonomous STE using mobile sensors due to robustness in noisy environments, yet their online action selection incurs substantial computational cost. Deep reinforcement learning (DRL) provides a promising alternative with its fast decision-making capability. In DRL-based STE, the agent selects actions based on belief states of the source term updated from noisy measurement sequences. However, existing methods rely on random exploration or solely on belief uncertainty reduction without an effective exploration strategy in DRL, which can limit policy robustness in noisy environments. To address this, we propose a curiosity-driven information-guided reinforcement learning for robust and efficient STE. The proposed method promotes active exploration of novel belief state transitions that have not been sufficiently explored during training. We further introduce an uncertainty-adaptive active perception reward to guide efficient source search under uncertainty. Simulations under high-noise conditions and real-world experiments demonstrate the robustness and feasibility of the proposed framework, highlighting its potential for practical STE problems.

Problem

• Source Term Estimation
• Deep Reinforcement Learning
• Noisy Environments
• Exploration Strategy
• Robustness

Innovation

• Curiosity-Driven
• Information-Guided Reinforcement Learning
• Uncertainty-Adaptive Reward
• Source Term Estimation