---
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)
- [Data Creation](#Data-Creation)
  - [Curation Rationale](##Curation-Rationale)
  - [Source Data](##Source-Data)
    - [Initial Data Collection and Normalization](###Initial-Data-Collection-and-Normalization)
    - [Who are the source data producers?](### Who-are-the-source-data-producers?)
    - [Annotations](###Annotations)
- [Considerations for Using the Data](#Considerations-for-Using-the-Data)
  - [Social Impact of Dataset](##Social-Impact-of-Dataset)
  - [Known Limitations](##Known-Limitations)
- [Additional Information](#Additional-Information)
  - [Licensing Information](##Licensing-Information)
  - [Citation Information](##Citation-Information)
  - [Contributions](##Contributions)

## 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
### Data Instances
A data point comprises tuples of sequences of (observations, actions, hidden_states):
```
{
    "obs":datasets.Array3D(),
    "actions":int,
    "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.

## 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/)