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Classroom Contents
Reinforcement Learning - Full Course Using Python
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- 1 - Start
- 2 - Introduction
- 3 - Gameplan
- 4 - RL in a Nutshell
- 5 - 1. Setup Stable Baselines
- 6 - 2. Environments
- 7 - Loading OpenAI Gym Environments
- 8 - Understanding OpenAI Gym Environments
- 9 - 3. Training
- 10 - Train a Reinforcement Learning Model
- 11 - Saving and Reloading Environments
- 12 - 4. Testing and Evaluation
- 13 - Evaluating RL Models
- 14 - Testing the Agent
- 15 - Viewing Logs in Tensorboard
- 16 - Performance Tuning
- 17 - 5. Callbacks, Alternate Algorithms, Neural Networks
- 18 - Adding Training Callbacks
- 19 - Changing Policies
- 20 - Changing Algorithms
- 21 - 6. Projects
- 22 - Project 1 Atari
- 23 - Importing Dependencies
- 24 - Applying GPU Acceleration with PyTorch
- 25 - Testing Atari Environments
- 26 - Vectorizing Environments
- 27 - Save and Reload Atari Model
- 28 - Evaluate and Test Atari RL Model
- 29 - Updated Performance
- 30 - Project 2 Autonomous Driving
- 31 - Installing Dependencies
- 32 - Test CarRacing-v0 Environment
- 33 - Train Autonomous Driving Agent
- 34 - Save and Reload Self Driving model
- 35 - Updated Self Driving Performance
- 36 - Project 3 Custom Open AI Gym Environments
- 37 - Import Dependencies for Custom Environment
- 38 - Types of OpenAI Gym Spaces
- 39 - Building a Custom Open AI Environment
- 40 - Testing a Custom Environment
- 41 - Train a RL Model for a Custom Environment
- 42 - Save a Custom Environment Model
- 43 - 7. Wrap Up
