Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative.

The vital part of every learning-based algorithm is an optimization procedure, e.g., Stochastic Gradient Descent.

The diversity and dynamism of the real world require reinforcement learning (RL) agents that can quickly adapt and learn new behaviors when placed in novel situations.

Value function factorization (VFF) is a popular approach to cooperative multiagent reinforcement learning in order to learn local value functions from global rewards.