Python Packages for Studying Reinforcement Learning - NBD Lite #33
Learn the reinforcement learning hands-on with these packages
Reinforcement Learning is a machine learning study field with concerns about optimal decision-making.
By learning from the environment, Reinforcement Learning would find the parameter that maximizes rewards.
Unlike supervised or unsupervised learning, Reinforcement Learning uses an independent learner (agent) to learn the environment without any labels or directions.
The learner must follow specific policies and reward systems set before learning.
Essentially, Reinforcement Learning requires four elements:
Learner
Environment
Policy Action
Reward
In general, there would be a valuable function in which the algorithm would try to maximize the reward average from learning the environment based on the action.
It might not sound straightforward initially, but we can try to learn Reinforcement Learning more intuitively.
We can learn more than just the theory using the Python packages presented in this article.
What are these packages? Let's get into it.
1. Pyqlearning
Pyqlearning is a Python package for reinforcement and deep reinforcement learning, especially for the Q-Learning method.
The package was designed to design a reinforcement learning algorithm rather than getting a single API for learning, so we can play around with the structure.
For studying reinforcement learning, Pyqlearning documentation is perfect. It outlines many of the basic to advanced processes in reinforcement learning.
The Pyqlearning documentation is structured with a description of the package and the theory behind the algorithm. You would learn a lot of concepts, including:
Epsilon Greedy Q-Learning
Boltzmann Q-Learning
Deep Reinforcement Learning
Deep Q-Network
The tutorial then teaches you how to develop the Reinforcement model. The tutorial was easy to follow and explained well to everyone.
If you want to install the package, use the following code.
pip install pyqlearningAnd if you can access the Pyqlearning demo, you can visit the following page.
2. ChainerRL
ChainerRL is a Python Package for Reinforcement Learning that implements the Chainer deep learning framework.
The package is easy to use, and the GitHub package provides many excellent learning materials.
We can install the package using the following code:
pip install chainerrl
ChaineRL also provides another package called ChainerRL-Visualizer to analyze how the agents work, making the debugger work easier.
We can install ChainerRL-Visualizer using the following code:
pip install chainerrl-visualizerFinally, the quick-start guide is the best place to start if you want to learn hands-on.
3. Coach
Coach is a Python Package for implementing Reinforcement Learning developed by Intel AI Lab. The package tries to provide a simple API and allow users to train the model in a few lines.
The getting started part of the documentation is the best part for your Reinforcement Learning study as it provides an easy-to-follow tutorial and the concept you need to understand Reinforcement Learning.
And if you want to follow an in-depth tutorial, you can always access the resources provided by Coach.
Finally, coach documentation provides a collection of Reinforcement Learning algorithms you can use.
4. Gymnasium
Gymnasium is a Python package developed by OpenAI that provides a standard API for Reinforcement Learning. Many other packages rely on the Gymnasium package, so its documentation is one of the most complete out there.
To install the package, we only need to run the following code.
pip install gymnasiumThe basic usage section is the best part for studying Reinforcement Learning using the Gymnasium package. This section explains the basic concept of using the package and Reinforcement Learning in detail.
The tutorials would be for you if you are more into hands-on learning.
That’s all my tips for studying Reinforcement Learning
Are there any more things you would love to discuss?Let'ss talk about it together!
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