Update README.md

offline-rl-algorithms/README.md

@@ -30,21 +30,22 @@ Current deep RL methods still typically rely on active data collection to succee
   * Difficulty in applying the algorithm: Due to the limited quality of the dataset, the learned strategy cannot be directly deployed in the production environment, and further online learning is required. How to design data sampling in the online training phase to avoid the sudden drop in the initial performance of the strategy due to the redundant data generated by the distribution change, and quickly converge to the optimal solution in a limited number of interactions?
 
 ### Contribution and Features
-This repository contains the codes of representative benchmarks and algorithms on the topic of Offline Reinforcement Learning. The repository is developed based on [d3rlpy](https://github.com/takuseno/d3rlpy) following MIT license to shed lights on the research on the above three challenges. While inheriting its advantages, the additional features include (or will be included):
- - **A unified algorithm framework with rich and fair comparisons bewteen different algorithms**:
-   - REDQ
-   - UWAC
-   - BRED
-   - …
- - **Abundant and real-world datasets**:
-   - Real-world industrial datasets
-   - Multimodal datasets
-   - Augmented datasets (and corresponding methods)
-   - Datasets obtained using representation learning (and corresponding methods)
- - **More easy-to-use log systems support**: 
-   - Wandb
-
-
+This repository contains the codes of representative benchmarks and algorithms on the topic of Offline Reinforcement Learning. The repository is developed based on [d3rlpy](https://github.com/takuseno/d3rlpy) following MIT license to shed lights on the research on the above three challenges. While inheriting its advantages, the additional features include (or will be included).
+
+ - For people who are insterested in Offline RL, our introduction of each algorithm and our tutorial blogs can be helpful.
+ - For RL practicers (especially who work on related fields), we provide advanced Offline RL algorithms with strong performance and different kinds of datasets. In detail:
+   - **A unified algorithm framework with rich and fair comparisons bewteen different algorithms**:
+     - REDQ
+     - UWAC
+     - BRED
+     - …
+   - **Abundant and real-world datasets**:
+     - Real-world industrial datasets
+     - Multimodal datasets
+     - Augmented datasets (and corresponding methods)
+     - Datasets obtained using representation learning (and corresponding methods)
+   - **More easy-to-use log systems support**: 
+     - Wandb
 ![Ecology of Offline RL](https://github.com/TJU-DRL-LAB/AI-Optimizer/blob/main/offline-rl-algorithms/Framework%20of%20Offline%20RL.png)
 
 ## Installation