From 0602fb885f4084a5f481f7d0c18d0235cc58ab93 Mon Sep 17 00:00:00 2001 From: Federico Berto Date: Sat, 8 Jun 2024 23:29:48 +0900 Subject: [PATCH] [Docs] upload new figures --- README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c61b33a6..7ecd0d3e 100644 --- a/README.md +++ b/README.md @@ -27,12 +27,21 @@ RL4CO is built upon: - [PyTorch Lightning](https://github.com/Lightning-AI/lightning): a lightweight PyTorch wrapper for high-performance AI research - [Hydra](https://github.com/facebookresearch/hydra): a framework for elegantly configuring complex applications -![RL4CO Overview](https://github.com/ai4co/rl4co/assets/34462374/4d9a670f-ab7c-4fc8-9135-82d17cb6d0ee) +![RL4CO-Overview](https://github.com/ai4co/rl4co/assets/48984123/0e409784-05a9-4799-b7aa-6c0f76ecf27f) +We offer flexible and efficient implementations of the following policies: +- **Constructive**: learn to construct a solution from scratch + - _Autoregressive (AR)_: construct solutions one step at a time via a decoder + - _NonAutoregressive (NAR)_: learn to predict a heuristic, such as a heatmap, to then construct a solution +- **Improvement**: learn to improve an pre-existing solution + +![RL4CO-Policy-Overview](https://github.com/ai4co/rl4co/assets/48984123/9e1f32f9-9884-49b9-b6cd-364861cc8fe7) We provide several utilities and modularization. For example, we modularize reusable components such as _environment embeddings_ that can easily be swapped to [solve new problems](https://github.com/ai4co/rl4co/blob/main/examples/3-creating-new-env-model.ipynb). -![RL4CO Policy](https://github.com/ai4co/rl4co/assets/48984123/ca88f159-d0b3-459e-8fd9-89799be9d1b0) +![RL4CO-Env-Embeddings](https://github.com/ai4co/rl4co/assets/48984123/c47a9301-4c9f-43fd-b21f-761abeae9717) + + ## Getting started Open In Colab