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

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

Torrent:	Udemy - Modern Reinforcement Learning: Deep Q Learning in PyTorch [Course Drive]
Description:	⚡️⚡️For More Udemy Courses Visit ?? Course Drive 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
Downloads:	67
Category:	Other /Tutorials
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Show Files »	Modern Reinforcement Learning- Deep Q Learning in PyTorch 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 (279.2 MB) 017 How to Read Deep Learning Papers.en.srt (11.5 KB) 017 How to Read Deep Learning Papers.mp4 (49.7 MB) 018 Analyzing the Paper.en.srt (31.7 KB) 019 How to Modify the OpenAI Gym Atari Environments.en.srt (18.9 KB) 019 How to Modify the OpenAI Gym Atari Environments.mp4 (81.8 MB) 020 How to Preprocess the OpenAI Gym Atari Screen Images.en.srt (1.7 KB) 020 How to Preprocess the OpenAI Gym Atari Screen Images.mp4 (18.6 MB) 021 How to Stack the Preprocessed Atari Screen Images.en.srt (2.1 KB) 021 How to Stack the Preprocessed Atari Screen Images.mp4 (24.5 MB) 022 How to Combine All the Changes.en.srt (1.4 KB) 022 How to Combine All the Changes.mp4 (9.2 MB) 023 How to Add Reward Clipping Fire First and No Ops.en.srt (4.8 KB) 023 How to Add Reward Clipping Fire First and No Ops.mp4 (30.6 MB) 024 How to Code the Agents Memory.en.srt (12.2 KB) 024 How to Code the Agents Memory.mp4 (61.4 MB) 025 How to Code the Deep Q Network.en.srt (11.4 KB) 025 How to Code the Deep Q Network.mp4 (66.4 MB) 026 Coding the Deep Q Agent Step 1 - Coding the Constructor.en.srt (7.3 KB) 026 Coding the Deep Q Agent Step 1 - Coding the Constructor.mp4 (39.8 MB) 027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.en.srt (2.3 KB) 027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.mp4 (15.3 MB) 028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.en.srt (2.9 KB) 028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.mp4 (31.1 MB) 029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.en.srt (9.4 KB) 029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.mp4 (38.0 MB) 030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.en.srt (14.5 KB) 030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.mp4 (73.0 MB) external-assets-links.txt (0.2 KB) ReadMe.txt (0.2 KB) Visit Coursedrive.org.url (0.1 KB) 01 Introduction 001 What You Will Learn In This Course.en.srt (7.1 KB) 001 What You Will Learn In This Course.mp4 (29.0 MB) 002 Required Background software and hardware.en.srt (5.7 KB) 002 Required Background software and hardware.mp4 (23.7 MB) 003 How to Succeed in this Course.en.srt (8.2 KB) 003 How to Succeed in this Course.mp4 (105.2 MB) 02 Fundamentals of Reinforcement Learning 004 Agents Environments and Actions.en.srt (13.5 KB) 004 Agents Environments and Actions.mp4 (57.8 MB) 005 Markov Decision Processes.en.srt (16.2 KB) 005 Markov Decision Processes.mp4 (60.5 MB) 006 Value Functions Action Value Functions and the Bellman Equation.en.srt (12.2 KB) 006 Value Functions Action Value Functions and the Bellman Equation.mp4 (47.2 MB) 007 Model Free vs. Model Based Learning.en.srt (5.3 KB) 007 Model Free vs. Model Based Learning.mp4 (25.3 MB) 008 The Explore-Exploit Dilemma.en.srt (8.0 KB) 008 The Explore-Exploit Dilemma.mp4 (37.9 MB) 009 Temporal Difference Learning.en.srt (23.4 KB) 009 Temporal Difference Learning.mp4 (129.5 MB) 03 Deep Learning Crash Course 010 Dealing with Continuous State Spaces with Deep Neural Networks.en.srt (24.3 KB) 010 Dealing with Continuous State Spaces with Deep Neural Networks.mp4 (105.3 MB) 011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.en.srt (8.7 KB) 011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.mp4 (44.2 MB) 012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.en.srt (9.2 KB) 012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.mp4 (60.1 MB) 013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.en.srt (6.3 KB) 013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.mp4 (45.7 MB) 014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.en.srt (1.7 KB) 014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.mp4 (18.7 MB) 015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.en.srt (4.4 KB) 015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.mp4 (18.9 MB) 016 Dealing with Screen Images with Convolutional Neural Networks.en.srt (5.9 KB) 016 Dealing with Screen Images with Convolutional Neural Networks.mp4 (19.8 MB) 05 Deep Reinforcement Learning with Double Q Learning 031 Analyzing the Paper.en.srt (23.6 KB) 031 Analyzing the Paper.mp4 (182.7 MB) 032 Coding the Double Q Learning Agent and Analyzing Performance.en.srt (9.6 KB) 032 Coding the Double Q Learning Agent and Analyzing Performance.mp4 (58.3 MB) external-assets-links.txt (0.1 KB) 06 Dueling Network Architectures for Deep Reinforcement Learning 033 Analyzing the Paper.en.srt (20.8 KB) 033 Analyzing the Paper.mp4 (134.0 MB) 034 Coding the Dueling Deep Q Network.en.srt (4.7 KB) 034 Coding the Dueling Deep Q Network.mp4 (23.6 MB) 035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.en.srt (13.0 KB) 035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.mp4 (70.6 MB) 036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.en.srt (6.9 KB) 036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.mp4 (37.3 MB) external-assets-links.txt (0.1 KB) 07 Improving On Our Solutions 037 Implementing a Command Line Interface for Rapid Model Testing.en.srt (14.4 KB) 037 Implementing a Command Line Interface for Rapid Model Testing.mp4 (57.1 MB) 038 Consolidating Our Code Base for Maximum Extensability.en.srt (19.6 KB) 038 Consolidating Our Code Base for Maximum Extensability.mp4 (168.8 MB) 08 Conclusion 039 Summarizing What Weve Learned.en.srt (7.3 KB) 039 Summarizing What Weve Learned.mp4 (35.5 MB) files
Added:	2020-06-09 11:09:13
Language:	English
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Release name:	Udemy - Modern Reinforcement Learning: Deep Q Learning in PyTorch [Course Drive]
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