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 that introduce Deep Q learning, Double Deep Q learning and Dueling Deep Q algorithms. You will then learn how to implement these in Pythonic and concise PyTorch and Tensorflow 2 code, which 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 match the specifications of the original Deep Q Learning articles. You will learn how to:
- Repeat steps to reduce computational overhead
- Resize Atari screen images to increase performance
- Stack frames to give the Deep Q agent a sense of motion
- To deal with the over-training model, evaluate the performance of the Deep Q operator with no random operation
- Clip rewards to enable Deep Q’s learning agent to generalize Atari games with different score 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 code Deep Q learning agents
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How to code Double Deep Q learning agents
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How to code 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 eager to learn about deep reinforcement learning
Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) course specifications
- Publisher: 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
Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) course prerequisites
- Precalculus
- Algebra
- Python
Course images
Sample video of the course
Installation guide
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English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Volume
1.74 GB
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