This Deep Reinforcement Learning 2.0 course is the perfect opportunity to learn and implement a new incredibly smart AI model. With this course, you will learn and understand the fundamentals of Artificial Intelligence, including Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more. You will also gain an in-depth understanding of the Twin-Delayed DDPG model and its training process. Finally, you will be able to implement the model from scratch, step by step, and practice coding exercises on the free and open source AI platform, Google Colab. Don't miss out on this amazing opportunity to master this highly advanced model!
Reinforcement Learning beginner to master - AI in Python
4.3
Udemy4,976 learners
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This Reinforcement Learning course on Udemy is the most comprehensive one available. It covers the three paradigms of modern artificial intelligence, and teaches you how to implement adaptive algorithms from scratch. You will learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning. This course will give you the foundation you need to understand new algorithms as they emerge, and prepare you for more advanced courses. It is focused on developing practical skills, and you will implement algorithms in jupyter notebooks from scratch. Don't miss this opportunity to master Reinforcement Learning and AI in Python!
Modern Reinforcement Learning: Actor-Critic Algorithms
4.5
Udemy2,791 learners
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This advanced course on deep reinforcement learning will teach you how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments. With a strong focus on dealing with environments with continuous action spaces, this course is perfect for those looking to do research into robotic control with deep reinforcement learning. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers, and master the answers to the fundamental questions in Actor-Critic methods. If you are a highly motivated and advanced student with prior course work in calculus, reinforcement learning, and deep learning, this course is for you.
Reinforcement Learning: AI Flight with Unity ML-Agents
4.8
Udemy965 learners
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This course is perfect for anyone interested in the intersection of video games and artificial intelligence. With Unity ML-Agents, you can watch your neural network learn in a real-time 3d environment based on rewards for good behavior. Learn how to use and train the example content, create custom assets with Blender, and build a full game with menus for level and difficulty selection. No prior knowledge of deep learning or reinforcement learning is required. By the end of the course, you'll have a complete game that you can share, add to your portfolio, or sell.
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