Emergent Failure Taxonomies: Automated Discovery of Unexpected RL Failure Modes via Cross-Domain Meta-Analysis

While Pham et al. (2024) and Xu et al. (2024) focus on RL-driven resilience in robotics, and Muhamad & Hussin (2024) survey RL fault-tolerance in fog/edge computing, there’s a rich opportunity to systematically uncover unexpected RL failures that transcend specific domains. This research would propose a meta-learning framework that aggregates logs and trajectories from diverse RL applications—robotics, supply chains (as in Thomas et al. 2022), edge systems, and more—to automatically extract, cluster, and taxonomize deviations from expected behaviors. Leveraging techniques from anomaly detection and deep representation learning, the system would highlight “emergent” failure patterns, not just those anticipated by designers. This cross-domain approach could reveal foundational blind spots in RL algorithms, leading to generalizable principles for robust RL design, and inspire new theoretical models for resilience. Unlike existing works that focus on isolated domains or manually identified failures, this idea emphasizes automated, scalable, and foundational failure analysis—potentially transforming how RL reliability is conceptualized and improved.

References:

1. An analysis of predictive maintenance strategies in supply chain management. Jubin Thomas, Piyush Patidar, Kirti Vinod Vedi, Sandeep Gupta (2022). International Journal of Science and Research Archive.
2. Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning. Tan-Hanh Pham, Godwyll Aikins, Tri Truong, Kim-Doang Nguyen (2024). Algorithms.
3. Meta-Reinforcement Learning of Hierarchical Fault-Tolerant Controller for Multiple Leg Failures in Hexapod robots. Tengye Xu, Zhe Yang, Qinyuan Ren (2024). 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM).
4. Governing Resource Failures through Reinforcement Learning Scheduling in Fog/Edge Computing: A Review. Aliyu Muhamad, M. Hussin (2024). 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS).