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license: apache-2.0 |
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# Dataset Card for Pong-v4-expert-MCTS |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Data Creation](#Data-Creation) |
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- [Curation Rationale](##Curation-Rationale) |
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- [Source Data](##Source-Data) |
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- [Initial Data Collection and Normalization](###Initial-Data-Collection-and-Normalization) |
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- [Who are the source data producers?](### Who-are-the-source-data-producers?) |
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- [Annotations](###Annotations) |
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- [Considerations for Using the Data](#Considerations-for-Using-the-Data) |
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- [Social Impact of Dataset](##Social-Impact-of-Dataset) |
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- [Known Limitations](##Known-Limitations) |
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- [Additional Information](#Additional-Information) |
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- [Licensing Information](##Licensing-Information) |
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- [Citation Information](##Citation-Information) |
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- [Contributions](##Contributions) |
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## Supported Tasks and Leaderboard |
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- TODO |
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## Dataset Description |
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This dataset includes 8 episodes of pong-v4 environment. The expert policy is EfficientZero, which is able to generate MCTS hidden states. |
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## Dataset Structure |
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### Data Instances |
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A data point comprises tuples of sequences of (observations, actions, hidden_states): |
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``` |
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{ |
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"obs":datasets.Array3D(), |
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"actions":int, |
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"hidden_state":datasets.Array3D(), |
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} |
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``` |
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## Source Data |
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### Data Fields |
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- `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. |
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- `actions`: An integer containing actions from 8 trajectories of an evaluated agent. This value is from 0 to 5. |
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- `hidden_state`: An Array3D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent. The data type is float32. |
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### Data Splits |
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There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator. |
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## Data Creation |
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### Curation Rationale |
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- 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). |
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### Source Data |
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#### Initial Data Collection and Normalization |
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- This dataset is collected by EfficientZero policy. |
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- Each return of 8 episodes is 20. |
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- No normalization is previously applied ( i.e. each value of observation is a uint8 scalar in [0, 255] ) |
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#### Who are the source language producers? |
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- [@kxzxvbk](https://huggingface.co/kxzxvbk) |
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#### Annotations |
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- The format of observation picture is [H, W, C], where the channel dimension is the last dimension of the tensor. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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- This dataset can be used for Imitation Learning, especially for algorithms that learn from a sequence. |
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- Very few dataset is open-sourced currently for MCTS based policy. |
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- This dataset can potentially promote the research for sequence based imitation learning algorithms. |
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### Known Limitations |
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- TODO |
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## Additional Information |
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### Licensing Information |
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- TODO |
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### Citation Information |
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``` |
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TODO |
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``` |
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### Contributions |
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Thanks to [@test](test_url), for adding this dataset. |
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[How to contribute to Datasets](https://files.pushshift.io/reddit/) |
