README.md

metadata

license: apache-2.0

Dataset Card for Pong-v4-expert-MCTS

Table of Contents

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

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

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.

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 for anyone who want to contribute to this dataset.