Reinforcement Learning in 3 Hours | Full Course using Python
Want to get started with Reinforcement Learning?
This is the course for you!
This course will take you through all of the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own environment!
In this video you'll learn:
1. All the basics to get up and started with Reinforcement Learning
2. How to build custom environments using OpenAI Gym
3. About working …
Watch on YouTube ↗
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Chapters (30)
Start
0:23
Introduction
1:15
Gameplan
4:24
RL in a Nutshell
13:30
1. Setup Stable Baselines
21:45
2. Environments
30:10
Loading OpenAI Gym Environments
40:00
Understanding OpenAI Gym Environments
42:58
3. Training
51:32
Train a Reinforcement Learning Model
1:00:00
Saving and Reloading Environments
1:04:23
4. Testing and Evaluation
1:06:35
Evaluating RL Models
1:09:34
Testing the Agent
1:15:56
Viewing Logs in Tensorboard
1:24:50
Performance Tuning
1:26:31
5. Callbacks, Alternate Algorithms, Neural Networks
1:27:39
Adding Training Callbacks
1:34:44
Changing Policies
1:38:27
Changing Algorithms
1:40:29
6. Projects
1:41:31
Project 1 Atari
1:41:51
Importing Dependencies
1:44:16
Applying GPU Acceleration with PyTorch
1:45:11
Testing Atari Environments
1:51:35
Vectorizing Environments
1:56:48
Save and Reload Atari Model
1:57:45
Evaluate and Test Atari RL Model
2:02:16
Updated Performance
2:06:34
Project 2 Autonomous Drivi
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