Update README.md

README.md

@@ -22,9 +22,16 @@ license: apache-2.0
   - [Citation Information](##Citation-Information)
   - [Contributions](##Contributions)
 
-## Supported Tasks and Leaderboard
+## Supported Tasks and Baseline
 
-- TODO
+- This dataset supports the training for Procedure Cloning algorithm.
+
+- Baseline
+
+  | Length for procedure sequence | Return |
+  | ----------------------------- | ------ |
+  | 0                             | 20     |
+  | 4                             | -21    |
 
 ## Dataset Description
 
@@ -45,7 +52,7 @@ A data point comprises tuples of sequences of (observations, actions, hidden_sta
 ### Data Fields
 
 - `obs`: An Array3D containing observations from 8 trajectories of an evaluated agent. The data type is uint8 and each value is in 0 to 255.
-- `actions`: An integer containing actions from 8 trajectories of an evaluated agent. This value is from 0 to 5.
+- `actions`: An integer containing actions from 8 trajectories of an evaluated agent. This value is from 0 to 5. Details about this environment can be viewed at [Pong - Gym Documentation](https://www.gymlibrary.dev/environments/atari/pong/).
 - `hidden_state`: An Array3D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent. The data type is float32.
 
 ### Data Splits
@@ -59,7 +66,7 @@ There is only a training set for this dataset, as evaluation is undertaken by in
 ### Source Data
 #### Initial Data Collection and Normalization
 - This dataset is collected by EfficientZero policy.
-- Each return of 8 episodes is 20.
+- The standard for expert data is that each return of 8 episodes is over 20.
 - No normalization is previously applied ( i.e. each value of observation is a uint8 scalar in [0, 255] )
 #### Who are the source language producers?
 - [@kxzxvbk](https://huggingface.co/kxzxvbk)
@@ -76,20 +83,30 @@ There is only a training set for this dataset, as evaluation is undertaken by in
 - This dataset can potentially promote the research for sequence based imitation learning algorithms.
 
 ### Known Limitations
-- TODO
+- This dataset is only used for academic research.
+- For any commercial use or other cooperation, please contact: opendilab@pjlab.org.cn
 
 ## Additional Information
 
-### Licensing Information
-- TODO
+### License
+This dataset is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
 
 ### Citation Information
 
 ```
-TODO
+@misc{Pong-v4-expert-MCTS,
+    title={{Pong-v4-expert-MCTS: OpenDILab} A dataset for Procedure Cloning algorithm using Pong-v4.},
+    author={Pong-v4-expert-MCTS Contributors},
+    publisher = {huggingface},
+    howpublished = {\url{https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS}},
+    year={2023},
+}
 ```
 
 ### Contributions
-Thanks to [@test](test_url), for adding this dataset.
+This data is partially based on the following repo, many thanks to their pioneering work:
+
+- https://github.com/opendilab/DI-engine
+- https://github.com/opendilab/LightZero
 
-[How to contribute to Datasets](https://files.pushshift.io/reddit/)
+Please view the [doc](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cardsHow) for anyone who want to contribute to this dataset.