From foundational concepts to advanced algorithms, this Nanodegree equips you with the tools to build intelligent agents using Python, neural networks, and state-of-the-art RL frameworks across robotics, finance, and beyond.
Overview
Syllabus
- Introduction to Deep Reinforcement Learning
- Value-Based Methods
- Combine cutting-edge reinforcement learning techniques with deep learning architectures. Use Q-networks and experience replay to train agents capable of navigating and adapting to real-time environments.
- Policy-Based Methods
- Multi-Agent Reinforcement Learning
- Special Topics in Deep Reinforcement Learning
- Neural Networks in PyTorch
- Computing Resources
- C++ Programming
Taught by
Alexis Cook, Arpan Chakraborty, Mat Leonard, Luis Serrano, Cezanne Camacho, Dana Sheahan, Chhavi Yadav, Juan Delgado, Miguel Morales, Bardia H., Ross A., Camilo G., Fabian V., Ho Chit S. and Robson M.
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Reviews
5.0 rating, based on 2 Class Central reviews
4.6 rating at Udacity based on 357 ratings
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Even though is quite tough, especially for someone coming from a different field than computer science, the course is very well structured and the contents are very well explained by the teacher, in addition all material provided (research papers, codes, descriptions and guides) are very helpful. It is exactly what I was looking for
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"The program is going really well, it has exceeded my expectations! I´ve learned a lot so far and I think this course will help me tackle real world problems in my organization.
Thank you Udacity for your support! "
