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Description
Advanced Reinforcement Learning in Python: from DQN to SAC, the advanced reinforcement learning course in Python from DQN to SAC has been published by Udemy Academy. This is the most complete advanced reinforcement learning course on Udemy. In this course, you will learn some of the most powerful deep learning algorithms in Python using PyTorch and PyTorch lightning. From scratch, you will implement adaptive algorithms that solve control tasks based on experience. You will learn to combine these techniques with neural networks and deep learning methods to create adaptive AI agents capable of solving decision-making tasks.
This course introduces you to the most advanced reinforcement learning techniques and also prepares you for the next courses in this series that examine other advanced methods that excel in other tasks. This course focuses on developing practical skills; Therefore, after learning the most important concepts of each family of methods, you implement one or more algorithms from scratch in jupyter notebooks.
Leveling modules:
- Reminder: Markov Decision Process (MDP)
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Reminder: Q-Learning
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Reminder: A brief introduction to neural networks
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Reminder: Deep Q-Learning
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Reminder: Policy gradient methods
Advanced reinforcement learning:
- PyTorch Lightning
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Hyperparameter adjustment with optona
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Deep learning for continuous action spaces (Normalized Advantage Function – NAF)
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Policy gradient Deep Determinant (DDPG)
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Delayed Conjugate DDPG (TD3)
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Simple actor-critic
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Hindsight Experience Replay (HER)
What you will learn
- You will get to know some of the most advanced reinforcement learning algorithms
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You will learn how to build artificial intelligence that can operate in a complex environment to achieve its goals.
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Create advanced reinforcement learning agents using the most popular Python tools
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Learn how to perform hyperparameter tuning (determining the best lab conditions for artificial intelligence learning)
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Understand the basic learning process of each algorithm.
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Debugging and development of presented algorithms.
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Understanding and implementing new algorithms from research articles
Who is this course suitable for?
- Developers who want to work in machine learning
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Data scientists and ML professionals looking to expand their knowledge base.
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Robotics students and researchers
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Engineering students and researchers
Advanced Reinforcement Learning in Python: from DQN to SAC
- Publisher: Udemy
- teacher : Escape Velocity Labs
- English language
- Education level: all levels
- Number of courses: 112
- Training duration: 8 hours and 5 minutes
Chapters of Advanced Reinforcement Learning in Python: from DQN to SAC 2022-12
Course prerequisites
- Be comfortable programming in Python
- Completing our course “Reinforcement Learning beginner to master” or being familiar with the basics of Reinforcement Learning (or
- watching the leveling sections included in this course).
- Know basic statistics (mean, variance, normal distribution)
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
2.37 GB
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