This course on Reinforcement Learning aims to teach learners the fundamentals of reinforcement learning using Python, OpenAI Gym, and Stable Baselines. By the end of the course, students will be able to build deep learning agents to solve various RL problems, create custom environments, and work on projects involving reinforcement learning. The course covers setting up Stable Baselines, working with environments in OpenAI Gym, training models, testing and evaluating agents, implementing callbacks and alternate algorithms, and completing projects such as Atari games, autonomous driving, and custom Open AI Gym environments. The intended audience for this course includes individuals interested in reinforcement learning, Python programming, and deep learning.
Reinforcement Learning - Full Course Using Python
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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