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In this course, you will learn :
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How to read and implement deep reinforcement learning papers
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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 tuning with command line arguments
Syllabus :
1. Fundamentals of Reinforcement Learning
- Agents, Environments, and Actions
- Markov Decision Processes
- Value Functions, Action Value Functions, and the Bellman Equation
- Model Free vs. Model Based Learning
- The Explore-Exploit Dilemma
- Temporal Difference Learning
2. Deep Learning Crash Course
- Dealing with Continuous State Spaces with Deep Neural Networks
- Naive Deep Q Learning in Code: Step 1 - Coding the Deep Q Network
- Naive Deep Q Learning in Code: Step 2 - Coding the Agent Class
- Naive Deep Q Learning in Code: Step 3 - Coding the Main Loop and Learning
- Naive Deep Q Learning in Code: Step 4 - Verifying the Functionality of Our Code
- Naive Deep Q Learning in Code: Step 5 - Analyzing Our Agent's Performance
- Dealing with Screen Images with Convolutional Neural Networks
3. Human Level Control Through Deep Reinforcement Learning: From Paper to Code
- How to Read Deep Learning Papers
- Analyzing the Paper
- How to Modify the OpenAI Gym Atari Environments
- How to Preprocess the OpenAI Gym Atari Screen Images
- How to Stack the Preprocessed Atari Screen Images
- How to Combine All the Changes
- How to Add Reward Clipping, Fire First, and No Ops
- How to Code the Agent's Memory
- How to Code the Deep Q Network
- Coding the Constructor
- Epsilon-Greedy Action Selection
- Memory, Model Saving and Network Copying
- The Agent's Learn Function
- The Main Loop and Analyzing the Performance
4. Deep Reinforcement Learning with Double Q Learning
- Analyzing the Paper
- Coding the Double Q Learning Agent and Analyzing Performance
5. Dueling Network Architectures for Deep Reinforcement Learning
- Analyzing the Paper
- Coding the Dueling Deep Q Network
- Coding the Dueling Deep Q Learning Agent and Analyzing Performance
- Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance
6. Improving On Our Solutions
- Implementing a Command Line Interface for Rapid Model Testing
- Consolidating Our Code Base for Maximum Extensability
- How to Test Our Agent and Watch it Play the Game in Real Time
