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⌨️ Reinforcement Learning Basics Agent and Environment
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Classroom Contents
Python Reinforcement Learning using OpenAI Gymnasium – Full Course
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- 1 ⌨️ Introduction
- 2 ⌨️ Reinforcement Learning Basics Agent and Environment
- 3 ⌨️ Introduction to OpenAI Gymnasium
- 4 ⌨️ Blackjack Rules and Implementation in Gymnasium
- 5 ⌨️ Solving Blackjack
- 6 ⌨️ Install and Import Libraries
- 7 ⌨️ Observing the Environment
- 8 ⌨️ Executing an Action in the Environment
- 9 ⌨️ Understand and Implement Epsilon-greedy Strategy to Solve Blackjack
- 10 ⌨️ Understand the Q-values
- 11 ⌨️ Training the Agent to Play Blackjack
- 12 ⌨️ Visualize the Training of Agent Playing Blackjack
- 13 ⌨️ Summary of Solving Blackjack
- 14 ⌨️ Solving Cartpole Using Deep-Q-NetworksDQN
- 15 ⌨️ Summary of Solving Cartpole
- 16 ⌨️ Advanced Topics and Introduction to Multi-Agent Reinforcement Learning using Pettingzoo
