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READEME.md ADDED
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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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+
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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.Array2D(),
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+ "actions":datasets.Array2D(),
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+ "hidden_state":datasets.Array2D(),
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+ }
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+ ```
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+ ### Data Fields
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+ - `obs`: An Array2D containing observations from 8 trajectories of an evaluated agent.
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+ - `actions`: An Array2D containing actions from 8 trajectories of an evaluated agent.
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+ - `hidden_state`: An Array2D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent.
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+
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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.
pong-v4-expert.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:fc3b6c39eb1c5c5bd719f450f2dabe0b4ac98657d54671c22f70da711899df61
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+ size 520535731