Reinforcement Learning
Learn the principles, algorithms, and applications of reinforcement learning in artificial intelligence.
Learn the principles, algorithms, and applications of reinforcement learning in artificial intelligence.
This module covers the basic concepts and terminology of reinforcement learning, including reward signals, value functions, and policy optimization.
This module covers the mathematical framework of Markov decision processes, which are commonly used to model sequential decision-making problems. Students will learn how to formulate and solve MDPs using dynamic programming and Monte Carlo methods.
This module covers recent advances in deep reinforcement learning, including deep Q-networks, policy gradients, and actor-critic methods. Students will learn how to train deep RL agents to solve complex tasks in simulated environments.
This module covers real-world applications of reinforcement learning, such as robotics, game playing, and autonomous driving. Students will learn how to adapt RL algorithms to different problem domains and evaluate their performance.
Bite-sized daily lessons that you can easily fit into your schedule. Each day, we release new lessons no longer than 15 minutes. Our lessons are carefully curated to ensure that they're both engaging and informative, allowing you to learn something new every day, and at your own pace.
Collaborate with other engineers from around the world, providing you with a unique opportunity to learn from others and build your professional network.
Our live learning sessions are designed to be interactive and engaging, giving you the opportunity to ask questions and interact with subject-matter experts.
Learn by solving real-world problems. Our courses are designed to get rid of the fluff and provide you with the most relevant information to help you apply your learning.
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