Summary
As engineering systems become more complex, it becomes progressively harder to obtain accurate physical models that describe their behavior. This had led to a shift from classical model-based control to data-driven and learning-based techniques, including reinforcement learning (RL). Despite the widespread use of RL algorithms across several domains, their design hinges crucially on the idealistic assumption of perfect feedback data for learning decision-making policies. However, in practice, data streams can be noisy with significant uncertainty ("heavy tailed probability distributions"), can
