Update README.md

README.md

@@ -562,68 +562,56 @@ class Objective:
 
 #
 #
-# GreedRL-CVRP-pretrained model
+# Getting started
 
+## Description
+We are delighted to release 🤠GreedRL Community Edition, as well as example of training and testing scripts for the standard Capacitated VRP (CVRP), you can download it and get started.
 
-## 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 default models for solving the Capacitated VRP (CVRP) with deep reinforcement learning(DRL).
-
-These models are limited by the training dataset, which may not generalize well for all use cases in different domains.
-
-## How to use
-
-### Requirements
-This library requires Python == 3.8. [Miniconda](https://docs.conda.io/en/latest/miniconda.html#system-requirements) / [Anaconda](https://docs.anaconda.com/anaconda/install/) is our recommended Python distribution.
+## Test environment
+🤠GreedRL Community Edition has been tested on Ubuntu 18.04 with GCC compiler v7.5.0 and CUDA version 11.4, and a [Miniconda](https://docs.conda.io/en/latest/miniconda.html#system-requirements) distribution with Python 3.8. We recommend using a similar configuration to avoid any possiblem compilation issue. 
 
+## Installation
+First, clone the repository.
 ```aidl
-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
+$ git clone https://huggingface.co/HUANG1993/GreedRL-VRP-pretrained-v1
 ```
-
-You need to compile first and add the resulting library `greedrl` to the `PYTHONPATH`
-
+Then, create and activate a python environment using conda, and install required packages.
 ```aidl
-python setup.py build
-
-export PYTHONPATH={root_path}/greedrl/build/lib.linux-x86_64-cpython-38/:$PYTHONPATH
+$ conda create -n python38 python==3.8
+$ conda activate python38
+$ pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu113
+```
+Finally, compile and add the resulting library `greedrl` to the `PYTHONPATH`
+```aidl
+$ python setup.py build
+$ export PYTHONPATH={your_current_path}/build/lib.linux-x86_64-cpython-38/:$PYTHONPATH
 ```
 
-
-### Training
+## CVRP Training
 
 1. Training data
 
-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.
+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, and each vehicle has a default capacity of 50.
 
 
 2. Start training
 ```python
-cd examples/cvrp
-
-python train.py --model_filename cvrp_5000.pt --problem_size 5000
+$ cd examples/cvrp
+$ python train.py --model_filename cvrp_5000.pt --problem_size 5000
 ```
 
-### Inference
+## CVRP Testing
 
-  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.
+After training process, you'll get a trained model, like `cvrp_5000.pt`, that you can use for test.
 
 ```python
-cd examples/cvrp
-
-python solve.py --device cuda --model_name cvrp_5000.pt --problem_size 5000
+$ cd examples/cvrp
+$ python solve.py --device cuda --model_name cvrp_5000.pt --problem_size 5000
 ```
 
-## Support
+# Support
 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](jiangwen.wjw@alibaba-inc.com).
 
-## About GreedRL
+# About GreedRL
 - Website: https://greedrl.github.io/