This curiosity drives my interest in exploring more advanced planning algorithms. My ability to represent complex problems, devise strategies under uncertainty, and implement algorithms in code has improved significantly. Understanding decision networks and policy iteration algorithms equips me to tackle real-world problems in automated systems. I faced a challenge while attempting the programming assignment where the actions’ outputs were reversed at first for moving the robot through the grid but I am glad that I was able to fix the code to move it up, right, then up and right again to reach the diamond target while avoiding the fire and blocked grid numbers. It was fun aligning the grid numbers with rewards that dictated the action movements based on the Markov decision process. The three important ideas that I am contemplating in relation to understanding decision processes are the balance between deterministic and stochastic planning, the integration of probabilistic models in decision-making, and the application of policy iteration in complex environments to develop robust AI systems capable of making intelligent decisions in diverse