Advanced Reinforcement Learning in Python: cutting-edge DQNs
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: From basic DQN to Rainbow DQN
Bullstrack
Advanced Reinforcement Learning in Python: cutting-edge DQNs udemy course
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: From basic DQN to Rainbow DQN
What you'll learn:
- Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)
- Understand and implement Evolution Strategies (ES) for AI
- Understand and implement DDPG (Deep Deterministic Policy Gradient)
Requirements:
- Know the basics of MDPs (Markov Decision Processes) and Reinforcement Learning
- Helpful to have seen my first two Reinforcement Learning courses
- Know how to build a convolutional neural network in Tensorflow
Description:
This is the most complete Advanced Reinforcement Learning course on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch 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 Artificial Intelligence agents capable of solving decision-making tasks.
This course will introduce you to the state of the art in Reinforcement Learning techniques. It will also prepare you for the next courses in this series, where we will explore other advanced methods that excel in other types of task.
The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.
Leveling modules:
- Refresher: The Markov decision process (MDP).
- Refresher: Q-Learning.
- Refresher: Brief introduction to Neural Networks.
- Refresher: Deep Q-Learning.
Advanced Reinforcement Learning:
- PyTorch Lightning.
- Hyperparameter tuning with Optuna.
- Reinforcement Learning with image inputs
- Double Deep Q-Learning
- Dueling Deep Q-Networks
- Prioritized Experience Replay (PER)
- Distributional Deep Q-Networks
- Noisy Deep Q-Networks
- N-step Deep Q-Learning
- Rainbow Deep Q-Learning
Who this course is for:
- Developers who want to get a job in Machine Learning.
- Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
- Robotics students and researchers.
- Engineering students and researchers.
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Course Details:
- 8.5 hours on-demand video
- 13 articles
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
Advanced Reinforcement Learning in Python: cutting-edge DQNs udemy free download
Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: From basic DQN to Rainbow DQN
Demo Link: https://www.udemy.com/course/advanced-deep-qnetworks/
