- Description:
D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets for training and benchmarking algorithms.
The datasets follow the RLDS format to represent steps and episodes.
Config description: See more details about the task and its versions in https://github.com/rail-berkeley/d4rl/wiki/Tasks#antmaze
Source code:
tfds.d4rl.d4rl_antmaze.D4rlAntmaze
Versions:
1.0.0
: Initial release.1.1.1
(default): Added v2 datasets.
Auto-cached (documentation): No
Feature structure:
FeaturesDict({
'steps': Dataset({
'action': Tensor(shape=(8,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'goal': Tensor(shape=(2,), dtype=float32),
'qpos': Tensor(shape=(15,), dtype=float32),
'qvel': Tensor(shape=(14,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(29,), dtype=float32),
'reward': float32,
}),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
steps | Dataset | |||
steps/action | Tensor | (8,) | float32 | |
steps/discount | Tensor | float32 | ||
steps/infos | FeaturesDict | |||
steps/infos/goal | Tensor | (2,) | float32 | |
steps/infos/qpos | Tensor | (15,) | float32 | |
steps/infos/qvel | Tensor | (14,) | float32 | |
steps/is_first | Tensor | bool | ||
steps/is_last | Tensor | bool | ||
steps/is_terminal | Tensor | bool | ||
steps/observation | Tensor | (29,) | float32 | |
steps/reward | Tensor | float32 |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Citation:
@misc{fu2020d4rl,
title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning},
author={Justin Fu and Aviral Kumar and Ofir Nachum and George Tucker and Sergey Levine},
year={2020},
eprint={2004.07219},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
d4rl_antmaze/umaze-v0 (default config)
Download size:
221.76 MiB
Dataset size:
274.83 MiB
Splits:
Split | Examples |
---|---|
'train' |
10,154 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/umaze-diverse-v0
Download size:
220.16 MiB
Dataset size:
270.23 MiB
Splits:
Split | Examples |
---|---|
'train' |
1,154 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/medium-play-v0
Download size:
220.40 MiB
Dataset size:
275.20 MiB
Splits:
Split | Examples |
---|---|
'train' |
10,695 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/medium-diverse-v0
Download size:
220.39 MiB
Dataset size:
271.18 MiB
Splits:
Split | Examples |
---|---|
'train' |
2,924 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/large-diverse-v0
Download size:
220.47 MiB
Dataset size:
273.36 MiB
Splits:
Split | Examples |
---|---|
'train' |
7,141 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/large-play-v0
Download size:
220.19 MiB
Dataset size:
276.61 MiB
Splits:
Split | Examples |
---|---|
'train' |
13,458 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/umaze-v2
Download size:
221.76 MiB
Dataset size:
274.83 MiB
Splits:
Split | Examples |
---|---|
'train' |
10,154 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/umaze-diverse-v2
Download size:
220.16 MiB
Dataset size:
270.20 MiB
Splits:
Split | Examples |
---|---|
'train' |
1,036 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/medium-play-v2
Download size:
220.40 MiB
Dataset size:
275.22 MiB
Splits:
Split | Examples |
---|---|
'train' |
10,768 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/medium-diverse-v2
Download size:
220.39 MiB
Dataset size:
271.19 MiB
Splits:
Split | Examples |
---|---|
'train' |
2,959 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/large-diverse-v2
Download size:
220.47 MiB
Dataset size:
273.38 MiB
Splits:
Split | Examples |
---|---|
'train' |
7,189 |
- Examples (tfds.as_dataframe):
d4rl_antmaze/large-play-v2
Download size:
220.18 MiB
Dataset size:
276.63 MiB
Splits:
Split | Examples |
---|---|
'train' |
13,517 |
- Examples (tfds.as_dataframe):