Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredictable battlefield conditions. In RL, noise can critically impact decision-making, mission success, and the safety of personnel. Reward machines offer a powerful tool to express complex reward structures in RL tasks, enabling the design of tailored reinforcement signals that align with mission objectives. This paper considers the problem of the robustness of intelligence-driven reinforcement learning based on reward machines. The preliminary results presented suggest the need for further research in evidential reasoning and learning to harden current state-of-the-art reinforcement learning approaches before being mission-critical-ready.

翻译：噪声鲁棒性在强化学习系统中至关重要，特别是在高风险与不确定性环境主导的军事场景中。噪声和不确定性是军事行动的固有特征，源于信息不完整、对抗性行动或战场条件不可预测等因素。在强化学习中，噪声可能对决策制定、任务成功及人员安全产生关键影响。奖励机器作为表达强化学习任务中复杂奖励结构的强大工具，能够设计与任务目标相符的定制化强化信号。本文探讨了基于奖励机器实现情报驱动强化学习的鲁棒性问题。初步结果表明，在强化当前最先进强化学习方法达到任务关键就绪状态之前，需进一步开展证据推理与学习领域的研究。