  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.
