---
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)
## Dataset Description
This dataset includes 8 episodes of pong-v4 environment. The expert policy is EfficientZero, which is able to generate MCTS hidden states.

## Supported Tasks and Leaderboards
## 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(),
}
```
### 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.

## Dataset Creation
### Curation Rationale
- TBD

### Source Data
#### Initial Data Collection and Normalization
- TBD
  
#### Who are the source language producers?
- TBD

#### Annotations
- TBD

## Considerations for Using the Data
### Known Limitations
- TBD

## Additional Information

### Licensing Information
- TBD

### Citation Information

```
TBD

```

### Contributions
Thanks to [@test](test_url), for adding this dataset.

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