Environment Engineering: The Foundation of RL Systems
In this course, we introduce the basic Reinforcement Learning (RL) framework and explore how to build a simple environment from scratch. We discuss states, actions, and rewards, then outline how an environment class should be structured and implement it.
Q-Learning Unleashed: Building Intelligent Agents
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.
Game On: Integrating RL Agents with Environments
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.
Navigating RL Challenges: Strategies and Future Directions
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.
