qgallouedec/prj_gia_dataset_metaworld_sweep_into_v2_1111

---
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

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

Then, load it with

```python
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'])
```

原始信息汇总

数据集概述

数据集名称

• 名称: prj_gia_dataset_metaworld_sweep_into_v2_1111

数据集标签

• 标签:
  • deep-reinforcement-learning
  • reinforcement-learning
  • gia
  • multi-task
  • multi-modal
  • imitation-learning
  • offline-reinforcement-learning

数据集内容

• 环境: 模仿学习环境，针对sweep-into-v2环境。
• 数据结构: 包含observations, actions, dones, rewards四个键值。

数据集加载

• 克隆命令: sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_sweep_into_v2_1111

• 加载代码: python 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])