| # 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. | |