Description
Modern Reinforcement Learning Course: Deep Q Agents (PyTorch & TF2). In this comprehensive deep learning course, you will learn a reproducible framework for reading and implementing deep reinforcement learning research papers. You will read original articles introducing deep Q learning, double deep Q learning, and dueling deep Q algorithms. Then, you will learn how to implement these in Pythonic and concise PyTorch and Tensorflow 2 code that can be extended to include any Q deep learning algorithm in the future. These algorithms will be used to solve various environments from the Atari Open AI library, including Pong, Breakout, and Bankheist. You will learn the key to making these deep Q learning algorithms work, i.e., how to modify the Atari Open AI Gym library to meet the specifications of the original deep Q learning articles. You will learn how to:
- Repeat the steps to reduce the computational effort
- Resize Atari screen images to improve performance
- Stack frames to give the Deep Q agent a sense of movement
- To deal with the overtraining model, evaluate the performance of the deep-Q operator without random operation.
- Clip rewards to enable Deep Q’s learning agent to generalize Atari games with different point scales
What you will learn in the Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) course
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How to read and implement Deep Reinforcement Learning articles
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How to program Deep-Q learning agents
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How to program Double Deep Q learning agents
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How to program Dueling Deep Q and Dueling Double Deep Q learning agents
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How to write modular and extensible deep reinforcement learning software
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How to automate hyperparameter setting with command line arguments
This course is suitable for people who
- Python developers are excited to learn more about deep reinforcement learning
Course specifications for Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2).
- Editor: Udemy
- Teacher: Phil Tabor
- Training level: beginner to advanced
- Training duration: 5 hours and 42 minutes
- Number of courses: 41
Course topics Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) on 8/2023
Course prerequisites for Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2).
- Preliminary calculation
- algebra
- python
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
English subtitles
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
1.7GB