Advanced Reinforcement Learning in Python: from DQN to SAC

🌠 Advanced Reinforcement Learning in Python: from DQN to SAC 🚀 GroupLayout: Advanced | Topics Covered: Reinforcement Learning, Deep Q-Learning, Policy Gradients, PyTorch & PyTorch Lightning, Neural Networks

Course Headline:

🧠 Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: DDPG, TD3, SAC, NAF, HER

Course Description:

Are you ready to embark on a journey into the realm of Advanced Reinforcement Learning? 🚀 Advanced Reinforcement Learning in Python: from DQN to SAC is the most complete course on Udemy, specifically designed for learners who are eager to implement some of the most potent Deep Reinforcement Learning algorithms from scratch.

📚 What You'll Learn:

• Master the implementation of cutting-edge deep reinforcement learning algorithms using Python and PyTorch.
• Understand how to combine neural networks with deep learning techniques to create intelligent agents that can make decisions based on experience.
• Explore state-of-the-art reinforcement learning methods and prepare for more advanced topics in future courses.

Why This Course?

• Practical Focus: You will dive into implementing algorithms such as DDPG, TD3, SAC, NAF, and HER directly within Jupyter notebooks.
• Hands-On Learning: After grasping the core concepts of each method, you'll roll up your sleeves to code these powerful agents from the ground up.

Course Highlights:

• 🎓 A comprehensive refresher on key concepts like the Markov decision process (MDP), Q-Learning, and policy gradient methods before diving into advanced techniques.
• 🤖 Deep dive into PyTorch Lightning and Hyperparameter tuning with Optuna to optimize your models effectively.
• 📈 Step-by-step guidance on implementing continuous action space solutions with Normalized advantage function (NAF), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor-Critic (SAC).
• ⚫️ Learn how to handle challenges such as sparse rewards with Hindsight Experience Replay (HER).

Key Takeaways:

• PyTorch Mastery: Develop deep expertise in PyTorch and PyTorch Lightning.
• Advanced Algorithms: Understand the workings of DDPG, TD3, SAC, NAF, and HER.
• Optimization Techniques: Gain proficiency in hyperparameter tuning with Optuna for optimal model performance.
• Real-World Applications: Apply your knowledge to create AI agents that can solve complex decision-making tasks.

By the end of this course, you'll be equipped with the advanced skills needed to tackle real-world problems using Deep Reinforcement Learning. Whether you're a data scientist, software engineer, or an AI enthusiast, this course will provide you with the tools and knowledge to push the boundaries of what artificial agents can achieve.

Module Breakdown:

Leveling Modules:

• 📚 Refresher: The Markov decision process (MDP)
• 📚 Refresher: Q-Learning
• 📚 Brief introduction to Neural Networks
• 📚 Refresher: Deep Q-Learning
• 📚 Refresher: Policy gradient methods

Advanced Reinforcement Learning:

• 🚀 PyTorch Lightning
• 🚀 Hyperparameter tuning with Optuna
• 🚀 Deep Q-Learning for continuous action spaces (Normalized advantage function - NAF)
• 🚀 Deep Deterministic Policy Gradient (DDPG)
• 🚀 Twin Delayed DDPG (TD3)
• 🚀 Soft Actor-Critic (SAC)
• 🚀 Hindsight Experience Replay (HER)

Embark on your journey to becoming an expert in Advanced Reinforcement Learning today! 🧠✨