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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.