Python Reinforcement Learning using OpenAI Gymnasium – Full Course

Dive into a comprehensive tutorial on reinforcement learning using OpenAI Gymnasium, a Python library for developing and comparing reinforcement learning algorithms. Learn the fundamentals of reinforcement learning, including agent-environment interactions, and explore practical implementations using the Gymnasium API. Start with solving the Blackjack environment, understanding concepts like epsilon-greedy strategy and Q-values. Progress to more complex scenarios by tackling the Cartpole problem using Deep Q-Networks (DQN). Gain hands-on experience through guided coding exercises, visualizations of agent training, and access to a full Google Colab notebook. Conclude with an introduction to advanced topics, including Multi-Agent Reinforcement Learning using Pettingzoo. Perfect for learners seeking to master reinforcement learning techniques and their real-world applications in Python.

⌨️ 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