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- READEME.md +27 -0
- pong-v4-expert.pkl +3 -0
READEME.md
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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.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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### Data Splits
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There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.
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pong-v4-expert.pkl
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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
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