Update README.md

README.md

@@ -4,36 +4,37 @@ pipeline_tag: reinforcement-learning
 tags:
 - Deep Reinforcement Learning
 - Combinatorial Optimization
-- Reinforcement Learning
 - Vehicle Routing Problem
 ---
 
-  ![](./images/GREEDRL-Logo-Original-640.png)
+![](./images/GREEDRL-Logo-Original-640.png)
 
 
 # 🤠GreedRL
 
+
 ## Overview
 
-- GreedRL is a Deep Reinforcement Learning(DRL) based solver that can solve various types of problems, such as TSP, VRPs(CVRP, VRPTW, VRPPD etc), Order Batching Problem, Knapsack Problem etc.
+- GreedRL is a Deep Reinforcement Learning (DRL) based solver that can solve various types of problems, such as TSP, VRPs (CVRP, VRPTW, VRPPD etc), Order Batching Problem, Knapsack Problem etc.
 
-- GreedRL achieves very high performance by accelerated by GPU while generating high qulity solutions. **1200 times faster** than [Google OR-Tools](https://developers.google.com/optimization) for large-scale (>=1000 nodes) CVRP, and the solution quality is improved by **about 3%**.
+- GreedRL achieves very high performance by running on GPU while generating high quality solutions.
+  **1200 times faster** than [Google OR-Tools](https://developers.google.com/optimization) for large-scale (>=1000 nodes) CVRP, and the solution quality is improved by **about 3%**.
 
 ## 🏆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).
-  
+- Entering the finalists of [INFORMS 2021 Franz Edelman Award](https://www.informs.org/Resource-Center/Video-Library/Edelman-Competition-Videos/2021-Edelman-Competition-Videos/2021-Edelman-Finalist-Alibaba)
+- Obtain [The Second Class Prize of Scientific and Technological Progress Award](https://www.ccf.org.cn/Awards/Awards/2022-11-08/776110.shtml).
 
 ## Editions
 
-We provide an open source Community Edition and an Enterprise Edition of our 🤠GreedRL for users.
+We have deliveried the following two editions of GreedRL for users.
 
-- **The Community Edition** is now released and available to [download](https://huggingface.co/Cainiao-AI/GreedRL).
-- **The Enterprise Edition** has a high-performance implementation that achives a faster computing speed, especially when solving larg-scale COPs. For more informations, please contact <a href="mailto:jiangwen.wjw@alibaba-inc.com">us</a>.
+- **The Community Edition** is open source and available to [download](https://huggingface.co/Cainiao-AI/GreedRL).
+- **The Enterprise Edition** has a higher performance implementation than **The Community Edition** (about 50 times faster), especially when solving larg-scale problems. For more informations, please contact <a href="mailto:jiangwen.wjw@alibaba-inc.com">us</a>.
 
 
 ## Architecture
-  ![](./images/GREEDRL-Framwork_en.png)
+![](./images/GREEDRL-Framwork_en.png)
 
 ## COPs Modeling examples
 
@@ -359,7 +360,7 @@ class Objective:
 
 ### Realistic Business Scenario
 <details>
-    <summary>real-time Dynamic Pickup and Delivery Problem (DPDP)</summary>
+    <summary>Instant Pickup and Delivery Service Problem (PDP)</summary>
 
 ```python
 from greedrl.feature import *
@@ -554,7 +555,7 @@ class Objective:
 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.
 
 ## 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. 
+🤠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.