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.

Welcome to the Reinforcement Learning Capstone. You've mastered the loop, the value, and the policy. Now, you will build a complete AI agent that masters a complex game from zero knowledge.

Your challenge is to select a high-dimensional environment, design a robust reward function, and train an agent using PPO or SAC to achieve superhuman performance.

You'll need to carefully shape the rewards. A landing is good, but a smooth landing that saves fuel is better. This is where your engineering intuition meets the machine's learning power.

Capstone Check: What is the most critical part of this final project?

• →Adding more layers to the network
• →Designing a reward function that accurately reflects the desired goal without causing unintended hacks

As your agent trains, you'll observe its evolution. From random flailing to purposeful movement, you are witnessing the birth of digital expertise.

Congratulations! You have completed the Reinforcement Learning track. You are now equipped to build systems that can learn, adapt, and succeed in the real world.

Final Check: What does it mean for an agent to be 'Superhuman' in this context?

• →It can talk like a human
• →It achieves a higher score or efficiency than an expert human player in the same task

Reinforcement Learning track complete! You've mastered the science of decision-making. Ready for the final frontiers of AI?

Compute a Real Discounted Return. Finish computing the full discounted return by working backward through the episode's rewards.