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READEME.md

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+# Dataset Card for Pong-v4-expert-MCTS
+## Table of Contents
+- [Dataset Description](#dataset-description)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+## 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.Array2D(),
+    "actions":datasets.Array2D(),
+    "hidden_state":datasets.Array2D(),
+}
+```
+### Data Fields
+- `obs`: An Array2D containing observations from 8 trajectories of an evaluated agent.
+- `actions`: An Array2D containing actions from 8 trajectories of an evaluated agent.
+- `hidden_state`: An Array2D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent.
+
+### Data Splits
+There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator.

pong-v4-expert.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc3b6c39eb1c5c5bd719f450f2dabe0b4ac98657d54671c22f70da711899df61
+size 520535731