TITAN is a novel hierarchical hybrid machine learning (H2ML) solution with a new type of mission-oriented and situation-aware hierarchical heterogeneous planning and scheduling structure. It can further develop a progression of the EMFG theory with relevant LS-MAS implementation techniques to manage the complexity of the LS-MAS. Considering the likelihood of developed work in practice, the implementation technique includes a novel Large Scale Multi-Agent Deep Reinforcement Learning (LS-MADRL) algorithm with an efficient Biologically Inspired Neuromorphic Computing System (BI-NCS).

TITAN characterizes the optimal efficiency of hybrid hierarchical actor-critic reinforcement learning (HAC-RL) through regret function. It characterizes computational complexity in terms of computation time.  TITAN develops a comprehensively efficient evaluation mechanism of Quality of Performance (QoP) metrics, and further finds optimal triggering time in hybrid safe HAC-RL.