Embark on an exciting journey to learn the fundamentals of reinforcement learning and its implementation using Gymnasium, the open-source Python library previously known as OpenAI Gym.
We just published a full course on the freeCodeCamp.org YouTube channel that will teach you the basics of reinforcement learning using Gymnasium.
Mustafa Esoofally created this course. He is an experienced machine learning engineer and course creator.
Gymnasium is an open source Python library maintained by the Farama Foundation. It offers a rich collection of pre-built environments for reinforcement learning agents, a standard API for communication between learning algorithms and environments, and a standard set of environments compliant with that API. This comprehensive video course is designed to help you understand reinforcement learning, a branch of machine learning that focuses on intelligent agents taking actions in an environment to maximize cumulative rewards.
This video course is carefully structured to provide you with a complete understanding of reinforcement learning, from basics to advanced topics:
Get an overview of the course, its objectives, and the topics we will cover.
- Reinforcement Learning Basics (Agent and Environment)
Learn about the fundamental concepts of reinforcement learning, including agents, environments, and their interactions.
- Introduction to Gymnasium
Discover the power of Gymnasium and how it can help you develop and test reinforcement learning algorithms.
- Blackjack Rules and Implementation in Gymnasium
Dive into the classic card game of Blackjack and learn how to implement it using Gymnasium.
- Solving Blackjack
Explore the process of solving Blackjack using reinforcement learning techniques.
- Install and Import Libraries
Learn how to set up your Python environment and import the necessary libraries for reinforcement learning.
- Observing the Environment
Understand how to monitor and interact with the environment during reinforcement learning tasks.
- Executing an Action in the Environment
Master the process of performing actions in the environment and receiving feedback.
- Understand and Implement Epsilon-greedy Strategy to Solve Blackjack
Learn the epsilon-greedy strategy, an essential technique for solving Blackjack with reinforcement learning.
- Understand the Q-values
Explore the concept of Q-values and how they are used in reinforcement learning algorithms.
- Training the Agent to Play Blackjack
Learn the process of training a reinforcement learning agent to play Blackjack effectively.
- Visualize the Training of Agent Playing Blackjack
Discover how to visualize and analyze the training process of a reinforcement learning agent.
- Summary of Solving Blackjack
Review the key concepts and techniques learned while solving Blackjack.
- Solving Cartpole Using Deep-Q-Networks (DQN)
Learn how to solve the classic Cartpole problem using Deep-Q-Networks, a popular reinforcement learning technique.
- Summary of Solving Cartpole
Recap the essential elements of solving Cartpole using reinforcement learning.
- Advanced Topics and Introduction to Multi-Agent Reinforcement Learning using Pettingzoo
Delve into advanced reinforcement learning topics, including multi-agent reinforcement learning and the use of the Pettingzoo library.
With this comprehensive video course, you'll be well-equipped to tackle reinforcement learning challenges using the powerful Gymnasium library.
Watch the full course on the freeCodeCamp.org YouTube channel (3-hour watch).