Update README.md

modelbased-rl/README.md

@@ -35,13 +35,18 @@ The current classifications of the mainstream algorithms in the modern Model-Bas
 
 ### Key Features
 
+#### Why MBRL ?
+
+Model-based reinforcement learning (MBRL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environments dynamics. 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. 
+
+#### Why Our Lib?
+
 With this repo and our research works, we want to draw the attention of RL community to studies on Model Based RL.
 
-- For people who are insterested in model based RL, our introduction in this repo and our [ZhiHu blog series](https://zhuanlan.zhihu.com/p/425318401) can be a preliminary tutorial.
+- For people who are insterested in model based RL, our introductions in this repo and our [ZhiHu blog series](https://zhuanlan.zhihu.com/p/425318401) can be a preliminary tutorial.
 
 - For Researchers in model-based RL, we collect several separate lines of research, which are sometimes closed-sourced or not reproducible and make some code-level optimizations for the convinience to find comparative baselines without the need to search around for implementations.
 
-We expect that our research thoughts and proposed topic for MBRL area can open up some new angles for future works on more advanced RL. **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.** 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. We are also looking forward to feedback in any form to promote more in-depth researches. See more [here](https://github.com/TJU-DRL-LAB/AI-Optimizer/tree/main/modelbased-rl).
 
 ## An Overall View of Research Works in This Repo