Update README.md

README.md

@@ -104,15 +104,22 @@ This repository contains the released codes of representative benchmarks and alg
 See more [here](https://github.com/TJU-DRL-LAB/transfer-and-multi-task-reinforcement-learning).
 
 ## Model-based Reinforcement Learning (MBRL)
-This repo contains a unified opensource code implementation for the Model-Based Reinforcement Learning methods. MBRL-Lib provides implementations of popular MBRL algorithms as examples of using this library. The current classifications of the mainstream algorithms in the modern Model-Based RL area are orthogonal, which means some algorithms can be grouped into different categories according to different perspectives. From the mainstream viewpoint,  we can simply divide `Model-Based RL` into two categories: `How to Learn a Model` and `How to Utilize a Model`.
+Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. By learning a model of the environment, model-based methods learn with significantly lower sample complexity.The model of the environment is a representation model that explicitly contains knowledge about the environment or the task, and generally two types of models are included: a transition model or a dynamics model and the reward model. Once this model is modeled, it can be properly integrated into the interaction with the environment and the learning of strategies. 
 
-- `How to Learn a Model` mainly focuses on how to build the environment model. 
+### Problems to Solve
+
+The current classifications of the mainstream algorithms in the modern Model-Based RL area are orthogonal, which means some algorithms can be grouped into different categories according to different perspectives. In this branch, we focus on two key questions  :`How to Learn a Model` and `How to Utilize a Model`.
 
+- `How to Learn a Model` mainly focuses on how to build the environment model. 
 - `How to Utilize a Model` cares about how to utilize the learned model. 
 
- Ignoring the differences in specific methods, the purpose of MBRL algorithms can be more finely divided into four directions as follows: `Reduce Model Error`、`Faster Planning`、` Higher Tolerance to Model Error` 、`Scalability to Harder Problems`.  For the problem of `How to Learn a Model`, we can study on reducing model error to learn a more accurate world model or learning a world model with higher tolerance to model error. For the problem of `How to Utilize a Model`, we can study on faster planning with a learned model or the scalability of the learned model to harder problems.  
+### Core Directions
+
+Ignoring the differences in specific methods, the purpose of MBRL algorithms can be more finely divided into four directions as follows: `Reduce Model Error`、`Faster Planning`、` Higher Tolerance to Model Error` 、`Scalability to Harder Problems`.  For the problem of `How to Learn a Model`, we can study on reducing model error to learn a more accurate world model or learning a world model with higher tolerance to model error. For the problem of `How to Utilize a Model`, we can study on faster planning with a learned model or the scalability of the learned model to harder problems.   
 
-![](./images/MBRL_framework.png)We have collected some of the mainstream MBRL algorithms and made some code-level optimizations. Bringing these algorithms together in a unified framework can save the researchers time in finding comparative baselines without the need to search around for implementations. Currently, we have implemented Dreamer, MBPO,BMPO, MuZero, PlaNet, SampledMuZero, CaDM and we plan to keep increasing this list in the future.  We will constantly update this repo to include new research made by TJU-DRL-Lab to ensure sufficient coverage and reliability. What' more, We want to cover as many interesting new directions as possible, and then divide it into the topic we listed above, to give you some inspiration and ideas for your RESEARCH. See more [here](https://github.com/TJU-DRL-LAB/AI-Optimizer/tree/main/modelbased-rl).
+![](./images/MBRL_framework.png)
+### Key Features
+Research in model-based RL has not been very standardized. It is fairly common for authors to experiment with self-designed environments, and there are several separate lines of research, which are sometimes closed-sourced or not reproducible. And for this, we have collected some of the mainstream MBRL algorithms and made some code-level optimizations. Bringing these algorithms together in a unified framework can save the researchers time in finding comparative baselines without the need to search around for implementations. Currently, we have implemented Dreamer, MBPO,BMPO, MuZero, PlaNet, SampledMuZero, CaDM and we plan to keep increasing this list in the future.  We will constantly update this repo to include new research made by TJU-DRL-Lab to ensure sufficient coverage and reliability. **What' more, We want to cover as many interesting new directions as possible, and then divide it into the topic we listed above, to give you some inspiration and ideas for your RESEARCH.** See more [here](https://github.com/TJU-DRL-LAB/AI-Optimizer/tree/main/modelbased-rl).
 
 # Contributing
 AI-Optimizer is still under development. More algorithms and features are going to be added and we always welcome contributions to help make AI-Optimizer better. Feel free to contribute.