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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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## 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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### 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. |
