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Udemy - Modern Reinforcement Learning: Deep Q Learning in PyTorch [Course Drive]

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09-06-2020 03:43

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File	Size
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/001 What You Will Learn In This Course.en.srt	7.12 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/001 What You Will Learn In This Course.mp4	29.02 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/002 Required Background software and hardware.en.srt	5.69 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/002 Required Background software and hardware.mp4	23.68 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/003 How to Succeed in this Course.en.srt	8.24 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/003 How to Succeed in this Course.mp4	105.19 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/004 Agents Environments and Actions.en.srt	13.48 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/004 Agents Environments and Actions.mp4	57.77 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/005 Markov Decision Processes.en.srt	16.20 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/005 Markov Decision Processes.mp4	60.48 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/006 Value Functions Action Value Functions and the Bellman Equation.en.srt	12.16 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/006 Value Functions Action Value Functions and the Bellman Equation.mp4	47.17 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/007 Model Free vs. Model Based Learning.en.srt	5.34 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/007 Model Free vs. Model Based Learning.mp4	25.29 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/008 The Explore-Exploit Dilemma.en.srt	8.04 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/008 The Explore-Exploit Dilemma.mp4	37.87 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/009 Temporal Difference Learning.en.srt	23.40 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/009 Temporal Difference Learning.mp4	129.49 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/010 Dealing with Continuous State Spaces with Deep Neural Networks.en.srt	24.28 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/010 Dealing with Continuous State Spaces with Deep Neural Networks.mp4	105.27 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.en.srt	8.66 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.mp4	44.16 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.en.srt	9.22 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.mp4	60.14 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.en.srt	6.26 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.mp4	45.75 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.en.srt	1.70 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.mp4	18.73 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.en.srt	4.37 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.mp4	18.92 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/016 Dealing with Screen Images with Convolutional Neural Networks.en.srt	5.87 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/016 Dealing with Screen Images with Convolutional Neural Networks.mp4	19.76 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/017 How to Read Deep Learning Papers.en.srt	11.54 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/017 How to Read Deep Learning Papers.mp4	49.72 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/018 Analyzing the Paper.en.srt	31.69 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/018 Analyzing the Paper.mp4	279.17 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/019 How to Modify the OpenAI Gym Atari Environments.en.srt	18.92 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/019 How to Modify the OpenAI Gym Atari Environments.mp4	81.79 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/020 How to Preprocess the OpenAI Gym Atari Screen Images.en.srt	1.68 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/020 How to Preprocess the OpenAI Gym Atari Screen Images.mp4	18.55 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/021 How to Stack the Preprocessed Atari Screen Images.en.srt	2.14 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/021 How to Stack the Preprocessed Atari Screen Images.mp4	24.53 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/022 How to Combine All the Changes.en.srt	1.41 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/022 How to Combine All the Changes.mp4	9.23 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/023 How to Add Reward Clipping Fire First and No Ops.en.srt	4.81 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/023 How to Add Reward Clipping Fire First and No Ops.mp4	30.62 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/024 How to Code the Agents Memory.en.srt	12.16 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/024 How to Code the Agents Memory.mp4	61.40 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/025 How to Code the Deep Q Network.en.srt	11.42 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/025 How to Code the Deep Q Network.mp4	66.44 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/026 Coding the Deep Q Agent Step 1 - Coding the Constructor.en.srt	7.29 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/026 Coding the Deep Q Agent Step 1 - Coding the Constructor.mp4	39.81 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.en.srt	2.26 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.mp4	15.26 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.en.srt	2.90 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.mp4	31.10 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.en.srt	9.44 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.mp4	38.00 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.en.srt	14.54 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.mp4	72.96 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/external-assets-links.txt	165.00 B
Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/031 Analyzing the Paper.en.srt	23.64 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/031 Analyzing the Paper.mp4	182.66 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/032 Coding the Double Q Learning Agent and Analyzing Performance.en.srt	9.64 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/032 Coding the Double Q Learning Agent and Analyzing Performance.mp4	58.28 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/external-assets-links.txt	89.00 B
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/033 Analyzing the Paper.en.srt	20.77 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/033 Analyzing the Paper.mp4	133.99 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/034 Coding the Dueling Deep Q Network.en.srt	4.66 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/034 Coding the Dueling Deep Q Network.mp4	23.55 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.en.srt	13.00 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.mp4	70.57 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.en.srt	6.94 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.mp4	37.26 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/external-assets-links.txt	110.00 B
Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/037 Implementing a Command Line Interface for Rapid Model Testing.en.srt	14.44 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/037 Implementing a Command Line Interface for Rapid Model Testing.mp4	57.15 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/038 Consolidating Our Code Base for Maximum Extensability.en.srt	19.61 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/038 Consolidating Our Code Base for Maximum Extensability.mp4	168.75 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/08 Conclusion/039 Summarizing What Weve Learned.en.srt	7.29 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/08 Conclusion/039 Summarizing What Weve Learned.mp4	35.46 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/09 Bonus Lecture/040 Bonus Video Where to Go From Here.en.srt	1.88 kB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/09 Bonus Lecture/040 Bonus Video Where to Go From Here.mp4	5.95 MB
Modern Reinforcement Learning- Deep Q Learning in PyTorch/ReadMe.txt	220.00 B
Modern Reinforcement Learning- Deep Q Learning in PyTorch/Visit Coursedrive.org.url	124.00 B
ReadMe.txt	220.00 B
Visit Coursedrive.org.url	124.00 B

Description

⚡️⚡️For More Udemy Courses Visit 👉🏻 https://coursedrive.org

Modern Reinforcement Learning: Deep Q Learning in PyTorch

How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

What you'll learn

• How to read and implement deep reinforcement learning papers
• How to code Deep Q learning agents
• How to Code Double Deep Q Learning Agents
• How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents
• How to write modular and extensible deep reinforcement learning software
• How to automate hyperparameter tuning with command line arguments

Requirements

• Some College Calculus
• Exposure To Deep Learning
• Comfortable with Python

Description

In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist.

You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:
• Repeat actions to reduce computational overhead
• Rescale the Atari screen images to increase efficiency
• Stack frames to give the Deep Q agent a sense of motion
• Evaluate the Deep Q agent's performance with random no-ops to deal with model over training
• Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales

If you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym.
We will cover:
• Markov decision processes
• Temporal difference learning
• The original Q learning algorithm
• How to solve the Bellman equation
• Value functions and action value functions
• Model free vs. model based reinforcement learning
• Solutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selection
Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym.

Who this course is for:

• Python developers eager to learn about cutting edge deep reinforcement learning