Update README.md

README.md

@@ -13,7 +13,6 @@ tags:
 
 # 🤠GreedRL
 
-
 ## Introduction
 
   ***Combinatorial Optimization Problems(COPs)*** has long been an active field of research. Generally speaking, there exists two main approachs for solving COPs, each of them having pros and cons. On the one hand, the *exact algorithms* can find the optimal solution, but they may be prohibitive for solving large instances because of the exponential increate of the execution time.
@@ -24,6 +23,9 @@ tags:
 
 ## 🏆Award
 
+  [INFORMS 2021 Franz Edelman Award finalists]( (https://www.informs.org/Resource-Center/Video-Library/Edelman-Competition-Videos/2021-Edelman-Competition-Videos/2021-Edelman-Finalist-Alibaba)) for Achievement in Operations Research and the Management Sciences (recognized for our work on Cainiao Network VRP algorithm).
+
+
 ## Main features
 
 * **GENERAL**
@@ -44,7 +46,8 @@ tags:
 ## COPs Modeling examples
 
 
-### Capacitated Vehicle Routing Problem (CVRP)
+### 
+Capacitated Vehicle Routing Problem (CVRP)
 <details>
     <summary>CVRP</summary>
 
@@ -551,19 +554,24 @@ class Objective:
 
 </details>
 
-# 
+## Pricing
+
 
 # GreedRL-CVRP-pretrained model
 
 
 ## Model description
 
+We are delighted to release 🤠GreedRL Community Edition, as well as pretrained models, which are specialized to CVRP with problem size ranging from 100 to 5000 nodes. 
 
+The model is trained using a deep reinforcement learning(DRL) algorithm known as REINFORCE. The model consists of two main components, an Encoder and a Decoder. The encoder produces embedding of all input nodes. The decoder then generates a solution sequence autoregressively. Feasibility of the solution is ensured by a *mask* procedure that prevents the model from selecting nodes that would result in a violation of constraints, e.g. exceeding the vehicle capacity.
 
 
 ## Intended uses & limitations
 
-You can use these model for solving the vehicle routing problems (VRPs) with reinforcement learning (RL).
+You can use these default models for solving the Capacitated VRP(CVRP) with deep reinforcement learning(DRL).
+
+These model is limited by its training dataset, this may not generalize well for all use cases in different domains.
 
 ## How to use
 
@@ -574,7 +582,7 @@ This library requires Python == 3.8. [Miniconda](https://docs.conda.io/en/latest
 pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
 ```
 
-You need to compile first and add the resulting library `greedrl_c` to the `PYTHONPATH`
+You need to compile first and add the resulting library `greedrl` to the `PYTHONPATH`
 
 ```aidl
 python setup.py build
@@ -585,13 +593,9 @@ export PYTHONPATH={root_path}/greedrl/build/lib.linux-x86_64-cpython-38/:$PYTHON
 
 ### Training
 
-We provide example of Capacitated VRP(CVRP) for training and inference.
-
 1. Training data
 
-We use the generated data for the training phase, the customers and depot locations are randomly generated in the unit square [0,1] X [0,1].
-
-For the CVRP, we assume that the demand of each node is a discrete number in {1,...,9}, chosen uniformly at random.
+We use generated data for the training phase, the customers and depot locations are randomly generated in the unit square [0,1] X [0,1]. For CVRP, we assume that the demand of each node is a discrete number in {1,...,9}, chosen uniformly at random.
 
 
 2. Start training
@@ -601,9 +605,9 @@ cd examples/cvrp
 python train.py --model_filename cvrp_5000.pt --problem_size 5000
 ```
 
-### Evaluation
+### Inference
 
-We provide some pretrained models for different CVRP problem sizes, such as `cvrp_100`, `cvrp_1000`, `cvrp_2000` and `cvrp_5000`, that you can directly use for inference.
+  We provide some pretrained models for different CVRP problem sizes, such as `cvrp_100` , `cvrp_1000` , `cvrp_2000` and `cvrp_5000`, that you can directly use for inference.
 
 ```python
 cd examples/cvrp
@@ -612,7 +616,9 @@ python solve.py --device cuda --model_name cvrp_5000.pt --problem_size 5000
 ```
 
 ## Support
-For commercial enquiries, please contact [us](huangxuankun.[email protected]).
+We look forward you to downloading it, using it, and opening discussion if you encounter any problems or have ideas on building an even better experience.
+
+For commercial enquiries, please contact [us]([email protected]).
 
 ## About GreedRL
 - Website: https://greedrl.github.io/