From b871788cd90b759ce261215127241fb65291a103 Mon Sep 17 00:00:00 2001 From: Rodrigo de Salvo Braz Date: Mon, 26 Feb 2024 15:59:00 -0800 Subject: [PATCH] Update tutorials on README.md Summary: This diff adds new tutorials to README.md, adding direct nbviewer links to them as well. Reviewed By: jb3618columbia Differential Revision: D54222857 fbshipit-source-id: 2e27ce20e5a8fc203e03f8bc33aae915d0a2fb25 --- README.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4f3b67b7..8e54c551 100644 --- a/README.md +++ b/README.md @@ -72,11 +72,18 @@ while not done: Users can replace the environment with any real-world problems. ## Tutorials -1. The first tutorial of Pearl focuses on recommender systems. We derived a small contrived recommender system environment using the MIND dataset (Wu et al. 2020). More details in https://github.com/facebookresearch/Pearl/tree/main/tutorials/single_item_recommender_system_example/single_item_recommender_system.ipynb -2. The second tutorial of Pearl focuses on contextual bandit algorithms and their implementation using Pearl library. We designed a contextual bandit environment based on UCI dataset and tested the performance of neural implementations of SquareCB, LinUCB, and LinTS. More details in https://github.com/facebookresearch/Pearl/tree/main/tutorials/contextual_bandits/contextual_bandits_tutorial.ipynb +We provide a few tutorial Jupyter notebooks (and are currently working on more!): + +1. [A single item recommender system](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/single_item_recommender_system_example/single_item_recommender_system.ipynb). We derived a small contrived recommender system environment using the MIND dataset (Wu et al. 2020). + +2. [Contextual bandits](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/contextual_bandits/contextual_bandits_tutorial.ipynb). 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. + +3. [Frozen Lake](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/frozen_lake/frozen_lake.ipynb). A simple example showing how to use a one-hot observation wrapper to learn the classic problem with DQN. + +4. [Deep Q-Learning (DQN) and Double DQN](https://nbviewer.org/github/facebookresearch/Pearl/blob/main/tutorials/sequential_decision_making/DQN_and_DoubleDQN_example.ipynb). Demonstrates how to run DQN and Double DQN on the Cart-Pole environment. + -More tutorials coming in 2024. ## Design and Features ![alt](./logo/agent_interface.png)