| | --- |
| | license: apache-2.0 |
| | --- |
| | # Dataset Card for Pong-v4-expert-MCTS |
| | ## Table of Contents |
| | - [Supported Tasks and Baseline](#support-tasks-and-baseline) |
| |
|
| | - [Data Usage](#data-usage) |
| |
|
| | - [Data Discription](##data-description) |
| | - [Data Fields](##data-fields) |
| | - [Data Splits](##data-splits) |
| | - [Initial Data Collection and Normalization](##Initial-Data-Collection-and-Normalization) |
| |
|
| | - [Additional Information](#Additional-Information) |
| |
|
| | - [Who are the source data producers?](##Who-are-the-source-data-producers) |
| | - [Social Impact of Dataset](##Social-Impact-of-Dataset) |
| | - [Known Limitations](##Known-Limitations) |
| |
|
| | - [Licensing Information](##Licensing-Information) |
| | - [Citation Information](##Citation-Information) |
| | - [Contributions](##Contributions) |
| |
|
| | ## Supported Tasks and Baseline |
| | - This dataset supports the training for [Procedure Cloning (PC )](https://arxiv.org/abs/2205.10816) algorithm. |
| | - Baselines when sequence length for decision is 0: |
| | | Train loss | Test Acc | Reward | |
| | | -------------------------------------------------- | -------- | ------ | |
| | |  | 0.90 | 20 | |
| | - Baselines when sequence length for decision is 4: |
| | | Train action loss | Train hidden state loss | Train acc (auto-regressive mode) | Reward | |
| | | ----------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------- | ------ | |
| | |  |  |  | -21 | |
| | ## Data Usage |
| | ### Data description |
| | This dataset includes 8 episodes of pong-v4 environment. The expert policy is [EfficientZero](https://arxiv.org/abs/2111.00210), which is able to generate MCTS hidden states. Because of the contained hidden states for each observation, this dataset is suitable for Imitation Learning methods that learn from a sequence like PC. |
| | ### Data Fields |
| | - `obs`: An Array3D containing observations from 8 trajectories of an evaluated agent. The data type is uint8 and each value is in 0 to 255. The shape of this tensor is [96, 96, 3], that is, the channel dimension in the last dimension. |
| | - `actions`: An integer containing actions from 8 trajectories of an evaluated agent. This value is from 0 to 5. Details about this environment can be viewed at [Pong - Gym Documentation](https://www.gymlibrary.dev/environments/atari/pong/). |
| | - `hidden_state`: An Array3D containing corresponding hidden states generated by EfficientZero, from 8 trajectories of an evaluated agent. The data type is float32. |
| |
|
| | This is an example for loading the data using iterator: |
| |
|
| | ```python |
| | from safetensors import saveopen |
| | |
| | def generate_examples(self, filepath): |
| | data = {} |
| | with safe_open(filepath, framework="pt", device="cpu") as f: |
| | for key in f.keys(): |
| | data[key] = f.get_tensor(key) |
| | |
| | for idx in range(len(data['obs'])): |
| | yield idx, { |
| | 'observation': data['obs'][idx], |
| | 'action': data['actions'][idx], |
| | 'hidden_state': data['hidden_state'][idx], |
| | } |
| | ``` |
| |
|
| | ### Data Splits |
| | There is only a training set for this dataset, as evaluation is undertaken by interacting with a simulator. |
| |
|
| | ### Initial Data Collection and Normalization |
| | - This dataset is collected by EfficientZero policy. |
| | - The standard for expert data is that each return of 8 episodes is over 20. |
| | - No normalization is previously applied ( i.e. each value of observation is a uint8 scalar in [0, 255] ) |
| |
|
| | ## Additional Information |
| |
|
| | ### Who are the source language producers? |
| |
|
| | [@kxzxvbk](https://huggingface.co/kxzxvbk) |
| |
|
| | ### Social Impact of Dataset |
| |
|
| | - This dataset can be used for Imitation Learning, especially for algorithms that learn from a sequence. |
| | - Very few dataset is open-sourced currently for MCTS based policy. |
| | - This dataset can potentially promote the research for sequence based imitation learning algorithms. |
| |
|
| | ### Known Limitations |
| |
|
| | - This dataset is only used for academic research. |
| | - For any commercial use or other cooperation, please contact: opendilab@pjlab.org.cn |
| |
|
| | ### License |
| | This dataset is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
| |
|
| | ### Citation Information |
| |
|
| | ``` |
| | @misc{Pong-v4-expert-MCTS, |
| | title={{Pong-v4-expert-MCTS: OpenDILab} A dataset for Procedure Cloning algorithm using Pong-v4.}, |
| | author={Pong-v4-expert-MCTS Contributors}, |
| | publisher = {huggingface}, |
| | howpublished = {\url{https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS}}, |
| | year={2023}, |
| | } |
| | ``` |
| |
|
| | ### Contributions |
| | This data is partially based on the following repo, many thanks to their pioneering work: |
| |
|
| | - https://github.com/opendilab/DI-engine |
| | - https://github.com/opendilab/LightZero |
| |
|
| | Please view the [doc](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cardsHow) for anyone who want to contribute to this dataset. |
