File size: 1,767 Bytes
0e2c753 96c14ad 509f603 96c14ad e422806 96c14ad e422806 96c14ad 509f603 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
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.
[How to contribute to Datasets](https://files.pushshift.io/reddit/) |