[Docs] upload new figures

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
 <a href="https://colab.research.google.com/github/ai4co/rl4co/blob/main/examples/1-quickstart.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>