RL Capstone in AI & Artificial Intelligence

For your capstone, you will choose an environment that requires complex control. Whether it's the LunarLander-v2 (balancing physics and fuel), an Atari game (visual feature extraction), or a Custom Business Simulation, the environment must provide a high-dimensional state space and a meaningful goal. You will be responsible for setting up the Gymnasium wrapper and ensuring the agent receives the necessary sensory data to succeed.

A 'Win' signal is rarely enough for fast learning. You will implement Reward Shaping to guide your agent through the early stages of training. You'll need to balance 'Positive' rewards (reaching the goal) with 'Penalty' signals (crashing, wasting time, or using excessive energy). Finding the right 'Incentive Structure' is what separates a world-class RL engineer from a hobbyist.

Once trained, you will evaluate your agent based on Mean Reward Over 100 Episodes. You will create a Learning Curve to visualize the training process and prove that your model has truly converged. Finally, you'll record a video of your agent in action, demonstrating its 'Superhuman' ability to navigate the world with precision and strategic foresight. This project is your graduation from the world of trial and error into the world of master engineering.