Deep Reinforcement Learning Tutorial for Python in 20 Minutes
Worked with supervised learning?
Maybe you’ve dabbled with unsupervised learning.
But what about reinforcement learning?
It can be a little tricky to get all setup with RL. You need to manage environments, build your DL models and work out how to save your models down so you can reuse them. But that shouldn’t stop you!
Because they’re powering the next generation of advancements in IOT environments and even gaming and the use cases for RL are growing by the minute. That being said, getting started doesn’t need to be a pain, you can get up and running in just 20 minutes working with Keras-RL and OpenAI.
In this video you’ll learn how to:
1. Create OpenAI Gym environments like CartPole
2. Build a Deep Learning model for Reinforcement Learning using Tensorflow and Keras
3. Train a Reinforcement Learning model using Deep Q Policy based learning using Keras-RL
Github Repo for the Project: https://github.com/nicknochnack/TensorflowKeras-ReinforcementLearning
P.s. Let me know how you go and drop a comment if you need a hand!