More tutorials are needed

[codeman6688]

There are so few tutorials that novices don’t know how to use this library

[rodrigodesalvobraz]

We will be addressing this as soon as possible. Thank you for the feedback.

[marcindulak]

@rodrigodesalvobraz please keep the issues open until they are resolved.

People subscribe to github issues like this one as a way to be notified about the developments in the project.

[rodrigodesalvobraz]

@rodrigodesalvobraz please keep the issues open until they are resolved.

People subscribe to github issues like this one as a way to be notified about the developments in the project.

Thanks for pointing that out, I will do that.

[swayson]

A tutorial covering each of the cells in this table would be really helpful to get a sense of things:)
Docstrings and function signatures also really helpful.

Pearl Features	Recommender Systems	Auction Bidding	Creative Selection
Policy Learning	✅	✅	✅
Intelligent Exploration	✅	✅	✅
Safety		✅
History Summarization		✅
Replay Buffer	✅	✅	✅
Contextual Bandit			✅
Offline RL	✅	✅
Dynamic Action Space	✅		✅
Large-scale Neural Network	✅

[rodrigodesalvobraz]

A tutorial covering each of the cells in this table would be really helpful to get a sense of things:) Docstrings and function signatures also really helpful.

Thank you, using the table as a guide is a really interesting suggestion. We are currently actively working on more tutorials. Please note that we have released a couple more recently. Here's the current list:

A single item recommender system. We derived a small contrived recommender system environment using the MIND dataset (Wu et al. 2020).

Contextual bandits. Demonstrates contextual bandit algorithms and their implementation using Pearl using a contextual bandit environment for providing data from UCI datasets, and tested the performance of neural implementations of SquareCB, LinUCB, and LinTS.

Frozen Lake. A simple example showing how to use a one-hot observation wrapper to learn the classic problem with DQN.

Deep Q-Learning (DQN) and Double DQN. Demonstrates how to run DQN and Double DQN on the Cart-Pole environment.