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license: apache-2.0

Dataset Card for Pong-v4-expert-MCTS

Table of Contents

[TOC]

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?

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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