Introduction to Reinforcement Learning (RL)
Maxime Vandegar
7:26:25
Description
Deep Reinforcement Learning in PyTorch: From Fundamentals to Advanced Algorithms
What You'll Learn?
- Core Concepts of Reinforcement Learning
- Implementing RL Algorithms in PyTorch
- Building Agents to Play Atari Games
- Exploring Policy-Based and Value-Based Methods
- Mastering Exploration vs. Exploitation
Who is this for?
What You Need to Know?
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DescriptionUnlock the world of Deep Reinforcement Learning (RL) with this comprehensive, hands-on course designed for beginners and enthusiasts eager to master RL techniques in PyTorch. Starting with no prerequisites, we’ll dive into foundational concepts—covering the essentials like value functions, action-value functions, and the Bellman equation—to ensure a solid theoretical base.
From there, we’ll guide you through the most influential breakthroughs in RL:
Playing Atari with Deep Reinforcement Learning – Discover how RL agents learn to master classic Atari games and understand the pioneering concepts behind the first wave of deep Q-learning.
Human-level Control Through Deep Reinforcement Learning – Take a closer look at how Deep Q-Networks (DQNs) raised the bar, achieving human-like performance and reshaping the field of RL.
Asynchronous Methods for Deep Reinforcement Learning – Explore Asynchronous Advantage Actor-Critic (A3C) methods that improved both stability and performance in RL, allowing agents to learn faster and more effectively.
Proximal Policy Optimization (PPO) Algorithms – Master PPO, one of the most powerful and efficient algorithms used widely in cutting-edge RL research and applications.
This course is rich in hands-on coding sessions, where you’ll implement each algorithm from scratch using PyTorch. By the end, you’ll have a portfolio of projects and a thorough understanding of both the theory and practice of deep RL.
Who This Course is For:
Ideal for learners interested in machine learning and AI, as well as professionals looking to add reinforcement learning with PyTorch to their skillset, this course ensures you gain the expertise needed to develop intelligent agents for real-world applications.
Who this course is for:
- AI Researchers and Academics
- Game Developers and Simulation Engineers
- Graduate Students in AI and Machine Learning
- Data Scientists and ML Engineers
- Beginner Machine Learning Enthusiasts
- Software Developers Exploring AI
Unlock the world of Deep Reinforcement Learning (RL) with this comprehensive, hands-on course designed for beginners and enthusiasts eager to master RL techniques in PyTorch. Starting with no prerequisites, we’ll dive into foundational concepts—covering the essentials like value functions, action-value functions, and the Bellman equation—to ensure a solid theoretical base.
From there, we’ll guide you through the most influential breakthroughs in RL:
Playing Atari with Deep Reinforcement Learning – Discover how RL agents learn to master classic Atari games and understand the pioneering concepts behind the first wave of deep Q-learning.
Human-level Control Through Deep Reinforcement Learning – Take a closer look at how Deep Q-Networks (DQNs) raised the bar, achieving human-like performance and reshaping the field of RL.
Asynchronous Methods for Deep Reinforcement Learning – Explore Asynchronous Advantage Actor-Critic (A3C) methods that improved both stability and performance in RL, allowing agents to learn faster and more effectively.
Proximal Policy Optimization (PPO) Algorithms – Master PPO, one of the most powerful and efficient algorithms used widely in cutting-edge RL research and applications.
This course is rich in hands-on coding sessions, where you’ll implement each algorithm from scratch using PyTorch. By the end, you’ll have a portfolio of projects and a thorough understanding of both the theory and practice of deep RL.
Who This Course is For:
Ideal for learners interested in machine learning and AI, as well as professionals looking to add reinforcement learning with PyTorch to their skillset, this course ensures you gain the expertise needed to develop intelligent agents for real-world applications.
Who this course is for:
- AI Researchers and Academics
- Game Developers and Simulation Engineers
- Graduate Students in AI and Machine Learning
- Data Scientists and ML Engineers
- Beginner Machine Learning Enthusiasts
- Software Developers Exploring AI
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Maxime Vandegar
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 36
- duration 7:26:25
- Release Date 2025/03/11
