This course teaches the basics of reinforcement learning and how to implement it using Gymnasium (OpenAI Gym). Students will learn about the agent-environment interaction, Q-values, epsilon-greedy strategy, and training agents to play Blackjack and Cartpole. The course covers deep Q-networks (DQN) and introduces multi-agent reinforcement learning using Pettingzoo. The teaching method includes hands-on coding tutorials and visualizations. The intended audience for this course includes individuals interested in machine learning, artificial intelligence, and Python programming.
Overview
Syllabus
⌨️ Introduction
⌨️ Reinforcement Learning Basics Agent and Environment
⌨️ Introduction to OpenAI Gymnasium
⌨️ Blackjack Rules and Implementation in Gymnasium
⌨️ Solving Blackjack
⌨️ Install and Import Libraries
⌨️ Observing the Environment
⌨️ Executing an Action in the Environment
⌨️ Understand and Implement Epsilon-greedy Strategy to Solve Blackjack
⌨️ Understand the Q-values
⌨️ Training the Agent to Play Blackjack
⌨️ Visualize the Training of Agent Playing Blackjack
⌨️ Summary of Solving Blackjack
⌨️ Solving Cartpole Using Deep-Q-NetworksDQN
⌨️ Summary of Solving Cartpole
⌨️ Advanced Topics and Introduction to Multi-Agent Reinforcement Learning using Pettingzoo
Taught by
freeCodeCamp.org
Related Courses
-
Reinforcement Learning Course - Full Machine Learning Tutorial
5.0 -
Reinforcement Learning Onramp
5.0 -
Practical AI with Python and Reinforcement Learning
-
Advanced AI: Deep Reinforcement Learning in Python
-
Fundamentals of Deep Reinforcement Learning
-
Practical Reinforcement Learning using Python - 8 AI Agents
