Python Reinforcement Learning using OpenAI Gymnasium – Full Course

This introductory course teaches reinforcement learning through practical Python implementations in Gymnasium. Learners build agents for Blackjack with Q-learning and for CartPole with deep Q-networks, with an introduction to multi-agent reinforcement learning.

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