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library_name: gia
tags:
  - deep-reinforcement-learning
  - reinforcement-learning
  - gia
  - multi-task
  - multi-modal
  - imitation-learning
  - offline-reinforcement-learning

An imitation learning environment for the sweep-into-v2 environment, sample for the policy sweep-into-v2

This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia

Load dataset

First, clone it with

git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_sweep_into_v2_1111

Then, load it with

import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_sweep_into_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])