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

   018 Analyzing the Paper.en.srt

   019 How to Modify the OpenAI Gym Atari Environments.mp4

   020 How to Preprocess the OpenAI Gym Atari Screen Images.mp4

   021 How to Stack the Preprocessed Atari Screen Images.mp4

   022 How to Combine All the Changes.mp4

   023 How to Add Reward Clipping Fire First and No Ops.mp4

   024 How to Code the Agents Memory.mp4

   025 How to Code the Deep Q Network.mp4

   026 Coding the Deep Q Agent Step 1 - Coding the Constructor.mp4

   027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.mp4

   028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.mp4

   029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.mp4

   030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.mp4

  ReadMe.txt

  01 Introduction

   001 What You Will Learn In This Course.mp4

   002 Required Background software and hardware.mp4

   003 How to Succeed in this Course.mp4

  02 Fundamentals of Reinforcement Learning

   004 Agents Environments and Actions.mp4

   005 Markov Decision Processes.mp4

   006 Value Functions Action Value Functions and the Bellman Equation.mp4

   007 Model Free vs. Model Based Learning.mp4

   008 The Explore-Exploit Dilemma.mp4

   009 Temporal Difference Learning.mp4

  03 Deep Learning Crash Course

   010 Dealing with Continuous State Spaces with Deep Neural Networks.mp4

   011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.mp4

   012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.mp4

   013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.mp4

   014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.mp4

   015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.mp4

   016 Dealing with Screen Images with Convolutional Neural Networks.mp4

  05 Deep Reinforcement Learning with Double Q Learning

   031 Analyzing the Paper.mp4

   032 Coding the Double Q Learning Agent and Analyzing Performance.mp4

  06 Dueling Network Architectures for Deep Reinforcement Learning

   033 Analyzing the Paper.mp4

   034 Coding the Dueling Deep Q Network.mp4

   035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.mp4

   036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.mp4

  07 Improving On Our Solutions

   037 Implementing a Command Line Interface for Rapid Model Testing.mp4

   038 Consolidating Our Code Base for Maximum Extensability.mp4

  08 Conclusion

   039 Summarizing What Weve Learned.mp4

  09 Bonus Lecture

   040 Bonus Video Where to Go From Here.mp4

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