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 Leaderboard

• TODO

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.
• 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.
• Each return of 8 episodes is 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

• TODO

Additional Information

Licensing Information

• TODO

Citation Information

TODO

Contributions

Thanks to @test, for adding this dataset.

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