Assessing the Robustness of Intelligence-Driven Reinforcement Learning
This addresses robustness issues in reinforcement learning for military applications, but it is incremental as it builds on existing reward machine methods.
The paper tackled the problem of robustness in intelligence-driven reinforcement learning using reward machines, particularly in military contexts, and found that current state-of-the-art approaches require further research to become mission-critical-ready.
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.