Learn the basics of reinforcement learning and how to implement it using Gymnasium (previously called OpenAI Gym). Gymnasium is an open source Python library originally created by OpenAI that provides a collection of pre-built environments for reinforcement learning agents. It provides a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. 💻 Google Colab Notebook (full tutorial code): https://colab.research.google.com/drive/1oNxRpRnht2Ogu5Id76YLLg_hkmwy-yKy?usp=sharing Gymnasium documentation: https://gymnasium.farama.org/ ✏️ Course developed by @EverythingTechWithMustaf 🔗 Mustaf on LinkedIn: https://www.linkedin.com/in/mustafa-esoofally-aab0501ab/ ⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:04:19) Reinforcement Learning Basics (Agent and Environment)
⌨️ (0:12:15) Introduction to OpenAI Gymnasium
⌨️ (0:14:59) Blackjack Rules and Implementation in Gymnasium
⌨️ (0:18:27) Solving Blackjack
⌨️ (0:19:46) Install and Import Libraries
⌨️ (0:23:19) Observing the Environment
⌨️ (0:27:55) Executing an Action in the Environment
⌨️ (0:33:01) Understand and Implement Epsilon-greedy Strategy to Solve Blackjack
⌨️ (0:42:28) Understand the Q-values
⌨️ (0:47:29) Training the Agent to Play Blackjack
⌨️ (0:57:10) Visualize the Training of Agent Playing Blackjack
⌨️ (1:04:34) Summary of Solving Blackjack
⌨️ (1:09:57) Solving Cartpole Using Deep-Q-Networks(DQN)
⌨️ (2:29:29) Summary of Solving Cartpole
⌨️ (2:34:07) Advanced Topics and Introduction to Multi-Agent Reinforcement Learning using Pettingzoo 🎉 Thanks to our Champion and Sponsor supporters:
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