Reinforcement Learning - Full Course Using Python

Overview

Dive into a comprehensive 3-hour video course on Reinforcement Learning using Python, OpenAI Gym, and Stable Baselines. Master the fundamentals of RL, from basic concepts to building custom environments. Learn to develop deep learning-powered agents capable of solving various RL problems, including CartPole, Breakout, and CarRacing. Explore topics such as environment setup, model training, evaluation, and performance tuning. Engage in hands-on projects, including Atari game playing, autonomous driving, and creating custom OpenAI Gym environments. Gain practical skills in applying GPU acceleration, vectorizing environments, and working with different algorithms and neural network policies. By the end of this course, acquire the knowledge and tools necessary to tackle a wide range of reinforcement learning challenges and create your own RL projects.

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