Update README.md

README.md

@@ -3,12 +3,14 @@ license: apache-2.0
 ---
 # Dataset Card for Pong-v4-expert-MCTS
 ## Table of Contents
-- [Dataset Description](#dataset-description)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
+[TOC]
+
+## Supported Tasks and Leaderboard
+
+- TODO
+
 ## Dataset Description
+
 This dataset includes 8 episodes of pong-v4 environment. The expert policy is EfficientZero, which is able to generate MCTS hidden states.
 
 ## Dataset Structure
@@ -21,10 +23,56 @@ A data point comprises tuples of sequences of (observations, actions, hidden_sta
     "hidden_state":datasets.Array3D(),
 }
 ```
+## Source Data
+
 ### 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.
 - `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
-There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
+There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
+
+## Data Creation
+
+### Curation Rationale
+
+- This dataset includes expert data generated by EfficientZero. Since it contains hidden states for each observation, it is suitable for Imitation Learning methods that learn from a sequence like [Procedure Cloning (PC)](https://arxiv.org/abs/2205.10816).
+### Source Data
+#### Initial Data Collection and Normalization
+- This dataset is collected by EfficientZero policy.
+- Each return of 8 episodes is 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)
+
+#### Annotations
+- The format of observation picture is [H, W, C], where the channel dimension is the last dimension of the tensor.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+- This dataset can be used for Imitation Learning, especially for algorithms that learn from a sequence.
+- Very few dataset is open-sourced currently for MCTS based policy.
+- This dataset can potentially promote the research for sequence based imitation learning algorithms.
+
+### Known Limitations
+- TODO
+
+## Additional Information
+
+### Licensing Information
+- TODO
+
+### Citation Information
+
+```
+TODO
+```
+
+### Contributions
+Thanks to [@test](test_url), for adding this dataset.
+
+[How to contribute to Datasets](https://files.pushshift.io/reddit/)