---
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 Baseline

- This dataset supports the training for Procedure Cloning algorithm.

- Baseline

  | Length for procedure sequence | Return |
  | ----------------------------- | ------ |
  | 0                             | 20     |
  | 4                             | -21    |

## 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. 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
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.
- 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)

#### 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
- This dataset is only used for academic research.
- For any commercial use or other cooperation, please contact: opendilab@pjlab.org.cn

## Additional Information

### License
This dataset is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

### Citation Information

```
@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
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

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.