In this course, we focus on building a Q-learning agent step by step. We start with the Bellman equation and the Q-table update, then implement a basic Q-learning function. Next, we incorporate an exploration policy (ε-greedy), and finally we demonstrate how to use the learned Q-table for decision making.

In this course, we integrate the grid-world environment with a Q-learning agent, focusing on agent-environment interaction and training over multiple episodes. We explore the exploration vs. exploitation tradeoff using an ε-greedy strategy and visualize performance through reward plots and policy displays.

The final course explores advanced techniques to enhance our RL systems. We'll implement random goal positions, hazardous environments with mines, and reward shaping, concluding with an exploration of cutting-edge RL developments that point toward future applications.