Reinforcement Learning - Full Course Using Python
Offered By: Nicholas Renotte via YouTube
Course Description
Overview
Syllabus
- Start
- Introduction
- Gameplan
- RL in a Nutshell
- 1. Setup Stable Baselines
- 2. Environments
- Loading OpenAI Gym Environments
- Understanding OpenAI Gym Environments
- 3. Training
- Train a Reinforcement Learning Model
- Saving and Reloading Environments
- 4. Testing and Evaluation
- Evaluating RL Models
- Testing the Agent
- Viewing Logs in Tensorboard
- Performance Tuning
- 5. Callbacks, Alternate Algorithms, Neural Networks
- Adding Training Callbacks
- Changing Policies
- Changing Algorithms
- 6. Projects
- Project 1 Atari
- Importing Dependencies
- Applying GPU Acceleration with PyTorch
- Testing Atari Environments
- Vectorizing Environments
- Save and Reload Atari Model
- Evaluate and Test Atari RL Model
- Updated Performance
- Project 2 Autonomous Driving
- Installing Dependencies
- Test CarRacing-v0 Environment
- Train Autonomous Driving Agent
- Save and Reload Self Driving model
- Updated Self Driving Performance
- Project 3 Custom Open AI Gym Environments
- Import Dependencies for Custom Environment
- Types of OpenAI Gym Spaces
- Building a Custom Open AI Environment
- Testing a Custom Environment
- Train a RL Model for a Custom Environment
- Save a Custom Environment Model
- 7. Wrap Up
Taught by
Nicholas Renotte
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