Haskell & Open AI Gym
Our Machine Learning series explores both basic and advanced topics when it comes to using Haskell and TensorFlow. This series expands on that knowledge and demonstrates how we can use Haskell with another practical AI framework. We'll explore some of the ideas behind the Open AI Gym, which provides a Python API to make agents for simple games. We can use this agent development process to teach ourselves more about AI and machine Learning. In this series, we'll replicate some of the simplest games and ideas using Haskell. We'll get an opportunity to use TensorFlow, both in Python and in Haskell.
Open AI Gym Primer: Frozen Lake
Well to our series on Haskell and the Open AI Gym! The Open AI Gym is an open source project for ...
Frozen Lake in Haskell
n part 1 of this series, we began our investigation into Open AI Gym. We started by using the Fro...
Open AI Gym: Blackjack
So far in this series, the Frozen Lake example has been our basic tool. In part 2, we wrote it in...
Basic Q-Learning
In the last two parts of this series, we've written two simple games in Haskell: Frozen Lake and ...
Generalizing Our Environments
In part 4 of this series, we applied the ideas of Q-learning to both of our games. You can compar...
Q-Learning with TensorFlow (Haskell)
In part 6 of the series, we used the ideas of Q-Learning together with TensorFlow. We got a more ...
Rendering with Gloss
Welcome to the final part of our Open AI Gym series! Throughout this series, we've explored some ...