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Reinforcement Learning with Gymnasium in Python

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Updated 12/2024
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
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PythonArtificial Intelligence4 hours15 videos52 exercises4,400 XP3,948Statement of Accomplishment

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Course Description

Discover the World of Reinforcement Learning

Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning. This interactive course takes you on a comprehensive journey through the core principles of RL where you'll master the art of training intelligent agents, teaching them to make strategic decisions and maximize rewards.

Master Essential Concepts and Tools

Your adventure starts with a deep dive into the unique aspects of RL. You'll not only learn foundational RL concepts but also apply key RL algorithms to practical scenarios using the renowned OpenAI Gym toolkit. This hands-on approach ensures a thorough grasp of RL essentials.

As your journey unfolds, you'll venture into the realms of advanced RL strategies to discover the intricacies of Monte Carlo methods, Temporal Difference Learning, and Q-Learning. By mastering these techniques in Python, you'll be adept at training agents for a variety of complex tasks.

Transform Your Learning into Real-World Impact

Concluding this course, you'll emerge with a profound understanding of RL theory, equipped with the skills to apply it creatively in real-world contexts. You'll be ready to build RL models in Python, unlocking a world of possibilities in your projects and professional endeavors.

Prerequisites

Supervised Learning with scikit-learnPython ToolboxIntroduction to NumPy
1

Introduction to Reinforcement Learning

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2

Model-Based Learning

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3

Model-Free Learning

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4

Advanced Strategies in Model-Free RL

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Reinforcement Learning with Gymnasium in Python
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