Playing Games with Reinforcement Learning

Overview

In this path, you'll embark on a journey to master the fundamentals of reinforcement learning by building intelligent agents that can autonomously navigate and learn how to act in complex environments.

Syllabus

Course 1: Environment Engineering: The Foundation of RL Systems
Course 2: Q-Learning Unleashed: Building Intelligent Agents
Course 3: Game On: Integrating RL Agents with Environments
Course 4: Navigating RL Challenges: Strategies and Future Directions

Courses

0 reviews
1 hour
View details

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.
0 reviews
1 hour
View details

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.
0 reviews
1 hour
View details

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.
0 reviews
1 hour
View details

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.